Mercator Ocean newsletter 17

Mercator Ocean Quarterly Newsletter #17 – April 2005 –

GIP Mercator Ocean

Quarterly Newsletter

Editorial

Dear Mercatorian,

By growing, Mercator resolutely turns towards users. Such logical development, which comes also within the wish of creation of the future operational centre, requires more than ever to offer quality products which will well reply to the downstream demand.

Correctly integrating observations in the assimilation system and qualifying their impact stay one of the key points to reach this objective.

The stake is double: to maintain/improve the operational system performance, we need to consolidate the present by demonstrating the importance of the ocean data measurements: satellite, Argo floats, moorings and others in situ measurement instruments, ... Furthermore, it is necessary to prepare the future by testing new assimilation methods, by estimating the future observation systems relevance and by developing strategy for their integration in the prototypes...

This Newsletter comes within this scope. The first article will describe the mean dynamic topography of the Mediterranean Sea, as a reference required for altimetric data assimilation. Current and future topographies are described, assessed and intercomparated in Mersea framework. The second article associates 4D-variational method and Argo drifting floats to examine the potential we may expect of vertical profiles of temperature and salinity to produce the oceanic state. Finally, the last article describes the Mercator strategy, developped in the scope of an ESA study, for the future surface salinity observation system: SMOS.

Among all of this, don't forget Europe, par excellence topically question at these days and which is approached in the News through the first annual Mersea meeting, held in Toulouse from March 29 to March 31st.

Have a good read and see you for next issue with regional and coastal oceanography topic!


GIP Mercator Ocean

Contents

News : MERSEA : en route to European operational oceanography By Pierre-Yves Le Traon

Page 3

The Mean Dynamic Topography used as a reference for altimetric data assimilation in the Mediterranean Sea By Fabrice Hernandez, Marie-Hélène Rio and Laurence Crosnier

Page 4 Potential of the ARGO network to produce an oceanic synthesis of the hydrology and the circulation in the North Atlantic using a 4D-variational method By Gaël Forget, Bruno Ferron and Herlé Mercier

Page 13 An observing system simulation experiment for SMOS: presentation of the study By Florence Birol, Pierre Brasseur, Lionel Renault, Charles-Emmanuel Testut and Benoît Tranchant

Page 17


News : MERSEA, en route to European operational oceanography

News : MERSEA, en route to European operational oceanography By Pierre-Yves Le Traon

The European project MERSEA aims at developing the ocean component of the GMES system (Global Monitoring for Environment and Security). It should lead to the setting up of a European Center for Ocean Monitoring and Forecasting (ECOMF) that will provide an integrated service for monitoring and forecasting the global and regional ocean. Many institutional or private applications are foreseen: environment monitoring, maritime transport and security, pollution monitoring and forecasting, sustainable management of ocean resources, offshore applications... MERSEA will allow us to address the requirements from policy makers and European and international treaties on the marine environment. The integrated description of the ocean state that MERSEA will provide will be, in addition, extremely beneficial to ocean, ecosystem and climate research.

MERSEA gathers about fifty European partners and the principal operational oceanography actors in Europe. MERSEA activities are separated into three main themes:

• In situ and satellite observing systems and provision of data directly useable by models.

• The design, implementation and evaluation of a co-ordinated set of monitoring and forecast systems covering the global ocean and the oceans and seas surrounding Europe.

• The development and demonstration of information products, applications and services.

MERSEA started in April 2004. The first annual meeting was held at the conference centre of Météo France from March 29 to March 31st. A good opportunity for partners to meet, analyze work progress and work plan for the next years. The guiding line in MERSEA is the definition of the final system (V3) that MERSEA will deliver in 2008 and the intermediate systems (V1, V2): models, assimilation methodologies, in situ, satellite and forcing data, interfaces with applications and services. These definitions are important milestones. They should allow the MERSEA steering committee to better target research activities. First R&D activities are already showing promising results: setting up of a model global configuration, new global and regional satellite products, improvements in the in situ monitoring systems, development of applications… At the same time, MERSEA contributes to the development and/or the consolidation of the infrastructure of operational oceanography in Europe: global and regional modelling/assimilation centres, data centres, data and product harmonization and distribution…

MERSEA steers well. Teams are in place and all partners share the same challenging objective: the setting up of a state-of-the art operational oceanography in Europe. The stakes for the future of oceanography are large and MERSEA must be a success. Over the next three years, MERSEA should pay particular attention to:

• Consolidating and sustaining the in situ and satellite observing systems. MERSEA must be proactive and should make recommendations and propositions to national and European agencies responsible for these systems. Impact studies should be carried out to demonstrate the impact of in situ/satellite data for ocean monitoring and forecasting (the very subject of this Newsletter). Sustaining a European contribution to Argo and developing operational oceanography satellite missions (high resolution altimetry, sea surface temperature, ocean colour) are among the most urgent priorities (e.g. ESA Blue Sentinel).

• Consolidating the architecture of the integrated system: in situ and satellite data centers, global and regional modeling and assimilation centers, transverse activities on data and product harmonization and distribution.

• Developing interfaces with applications from the institutional and private sectors. MERSEA will provide the backbone information (core services) on the ocean state (hindcast, nowcast and forecast) required by applications (downstream services). Some applications are developed directly in MERSEA to develop and test interfaces while others are developed through the ESA GSE (GMES Service Element) projects or national projects.

• Answering requirements from European agencies (e.g. EEA, - European Environment Agency, EMSA - European Maritime Safety Agency-) and policies/treaties on environment monitoring and maritime security (e.g. IPCC - Intergovernmental Panel on Climate Change -, OSPAR - Commission for the Protection of the Marine Environment of the North-East Atlantic -, HELCOM - Baltic Marine Environment Protection Commission-, ICES - International Council for the exploration of the Sea-, Water Framework Directive). MERSEA should, in particular, develop and monitor a series of relevant indicators on the ocean state.


The Mean Dynamic Topography used as a reference for altimetric data assimilation in the Mediterranean Sea

The Mean Dynamic Topography used as a reference for altimetric data assimilation in the Mediterranean Sea By Fabrice Hernandez, Marie-Hélène Rio and Laurence Crosnier

Introduction

The Mean Dynamic Topography (henceforth MDT) needs to be defined accurately for operational oceanography as it provides the appropriate mean height to add to altimetric Sea Level Anomalies (SLA) prior any assimilation of the absolute sea level. This paper will discuss three different topics around the MDT in the Mediterranean Sea. The first section written by F. Hernandez describes the MDT presently used in the MERCATOR PSY2v1 operational system. The second section, written by M.H. Rio, presents in detail a new estimation by (Rio et al., 2005) of the MDT thanks to in situ measurements, altimetric data and a general circulation model. This synthetic MDT estimation, hereafter called MED-RIO05, is likely to be used by MERCATOR in a near future. The third part, written by L. Crosnier, deals with the differences between MDT within the MERSEA project and the MED-RIO05 MDT. It also presents the impact of the MDT amplitude in the Gulf of Lion on winter convection.

The operational MERCATOR PSY2v1 Mean Dynamic Topography

By Fabrice Hernandez, with the contribution of Eric Greiner

The MERCATOR PSY2v1 prototype over the Mediterranean Sea was first using a three years averaged MDT of the PAM-5 simulation (free run forced with a perpetual year for atmospheric fluxes). This first MDT is presented in the annex of Newsletter8 (January 2003). It was not realistic and causing strong defaults in the system circulation. Others MDT issued from more recent model simulations all had a different circulation pattern, as shown in Figure 1. However, some patterns were also realistic.

MED-16 - ERA-40 ERA-40 forcing 1990 à 2000 (free slip)

MED-1998-2002 ECMWF forcing (no-slip)

PAM22 – one year averaged in 1999

PAM22 – one year averaged in 2001

Figure 1 Mean Dynamic Topographies from four model simulations. MED-16 based on OPA model (from K. Béranger). PAM-22 is the

PAM simulation forced with ECMWF atmospheric fluxes (from R. Bourdalle-Badie and Y. Drillet)



Thus we tested the circulation patterns of the four surfaces by comparison to hydrological data. Each MDT plus the concurrent SLA is compared to the corresponding hydrographic absolute dynamic heights (same time and position than the SLA). This technique was developed by (Rio and Hernandez, 2004):

)t,x(h)t,x(SLA)x(TDM dyni ↔+

The residual differences: ( )SLATDMh +− are direct values of the MDT relative precision. At a given hydrographic data

location, the comparison of the four residual corresponding to the four MDT give the relative accuracy among these four surfaces, whatever are the SLA and hydrographic data errors. Note that we assume here no correlation between the MDT and SLA or hydrographic data error budgets.

In conclusion, we have determined a composite MDT (Figure 5, upper left panel) based on the four model MDT using the relative differences as weights in the linear combination. Note also that taking into account the residuals allow to reduce interannual differences between the expected mean value (the 1993-99 period over which the altimetric data have been averaged) and the mean represented in each of the four surfaces, corresponding to averaging over shorter or shifted periods. After presenting in this section the MDT presently used in the MERCATOR system, we will present in the next section the best MDT candidate to be used in the near future in the MERCATOR system.

A synthetic Mean Dynamic Topography of the Mediterranean Sea estimated from the combination of altimetric data, in situ measurements and a general circulation model, by M.H. Rio

By Marie-Hélène Rio

Introduction

The Mediterranean MDT (Figure 1) described in this section was computed for the period 1993-1999 using a method described and applied for the global ocean in (Rio and Hernandez, 2004). It is based on a synthetic technique which consists in subtracting the oceanic variability as measured by altimetry to in situ measurements of the absolute oceanographic signal in order to compute local “synthetic” estimates of the Mediterranean MDT. These synthetic estimates and the associated errors are then used to correct a first guess of the mean field and map the MDT (and the corresponding mean geostrophic circulation) on a 1/8th degree regular grid of the whole Mediterranean basin using a multivariate objective analysis (Bretherton et al., 1976; Le Traon and Hernandez, 1992). Methods and results are thoroughly described in (Rio et al., 2005). In this note, we briefly describe the different datasets used and mainly focus on a quantitative validation of the obtained Mediterranean Synthetic Mean Dynamic Topography (SMDT), already named MED-RIO05, using independent in situ and altimetric data.

Figure 2

Synthetic Mean Dynamic Topography MED-RIO05 computed in this paper. Main features of the mean circulation are superimposed schematically on the SMDT plot (see text for abbreviation definition)



Data

Three different datasets are needed for the estimation: altimetric data, in situ measurements, and an existing ‘first guess’ of the Mediterranean MDT. The altimetric data available for this study are 1/8th degree weekly maps of (SLA) relative to a seven year (1993-1999) mean profile computed at CLS and distributed by AVISO. Corresponding maps of geostrophic velocity anomalies are computed by simple differentiation between adjacent grid points.

The in situ dataset consists in more than 120000 velocity measurements from satellite-tracked surface drifters available from 1 January 1993 to 11 November 1999 (Mauerhan, 2000; Poulain et al., 2004). The only geostrophic component of the in situ velocities is extracted first applying a 36 hours low pass filter and further removing the Ekman component estimated using a model by (Mauri and Poulain, 2004).

The first guess used in this study is the average over the period 1993-1999 of outputs from a MFSTEP (Mediterranean Forecasting System: Toward Environmental Predictions) model run with no data assimilation (Demirov et al., 2003).

Validation using independent in situ data

Hydrological profiles

A total of 16 hydrological profiles were measured during the NORBAL-2 campaign in the Bonifacio gyre area, between the 7th December 2001 and 11th December 2001. Figure 3 shows the altimetric SLA obtained averaging the maps available for the 5th and 12th December 2001 (Figure 3a), as well as the corresponding absolute altimetric sea level maps computed using the first guess (Figure 3b) or the MED-RIO05 SMDT (Figure 3c). The dynamic heights computed in the area relative to 500, 1000 and 1500 m are superimposed on the three plots respectively as squares, circles and triangles (for each reference depth, the mean of the dynamic heights is readjusted to the SMDT mean). In all three cases, the Bonifacio gyre is clearly visible. However, when considering only the altimetric SLA (Figure 3a) the gyre intensity is underestimated. The use of the first guess allows to increase the gyre intensity but the core of the structure is still 5-6 cm higher than obtained by in situ measurements. The use of the SMDT clearly allows to better reproduce the position and intensity of the gyre.

Figure 3

Absolute altimetric height in the Bonifacio gyre area, for the period 5th to 12th December 2001, computed using to reference the altimetric anomalies: a) a zero mean field, b) the first guess, and c) the MED-RIO05 SMDT. In situ dynamic heights measured in

the area are superimposed as squares if computed relative to 500 m, as circles relative to 1000m, and as triangles relative to 1500m.

Sea Surface Temperature satellite data

We display on Figure 4 the maps of SLA and SMDT+SLA obtained in the Alboran Sea on November, 27th, 2002 and January, 8th, 2003 as well as the corresponding maps of Sea Surface Temperature (SST) as obtained from NOAA-16 and -17 AVHRR



data. On both days the anomaly maps feature a cyclonic structure on the western side of the Alboran Sea, coupled with an anticyclonic structure on the eastern side. The use of the SMDT to compute absolute altimetric sea level allows to highlight a more complex situation: on November 27th , the absolute altimetric map clearly features a large meandering current all through the Alboran Sea, with the WAG (Western Alboran Gyre) and EAG (Eastern Alboran Gyre) anticyclonic gyres, centered at 4.4°E and 2.2°E, in very good agreement with SST data. On January 8th , the WAG has moved further east (centered at 3°E), the EAG is still visible on 2°E and a new meander has formed at the western extremity of the area (centered at 4.8°E). Consequently, on that day, three simultaneous anticyclonic gyres are present in the Alboran Sea. This is once more in very good agreement with what can be see on the SST image obtained on the same day.

Figure 4

Maps of altimetric anomalies (top), absolute altimetric signal (middle) and Sea Surface Temperature (bottom) in the Alboran sea on November 27th 2002 (left) and January 8th 2003 (right)

Drifting buoy velocities

Several surface drifting buoys have been deployed in the Adriatic from September 2002 to June 2003 as part of the DOLCEVITA program (Lee, 2004) providing nearly 6400 velocity measurements in the Adriatic and Ionian Seas. Absolute altimetric velocities at the time and position of the drifting buoy measurements are computed using various MDTs to reference altimetric anomalies and RMS differences to the in situ absolute velocities are then computed. Results are displayed in Table 1. The use of the MED-RIO05 SMDT allows to reduce the RMS differences and increase the regression slope between altimetric and in situ velocities with respect to the use of the first guess or a zero-mean field.

Moreover, comparison was done to evaluate the impact of present GRACE (Gravity recovery and Climate Experiment) and future GOCE (Gravity Field and Steady-State Ocean Circulation Explorer) geoid models to retrieve the Mediterranean MDT. A large scale MDT was estimated from GRACE data subtracting the EIGEN-GRACE02S geoid from the altimetric Mean Sea Surface CLS01 (Hernandez and Schaeffer, 2001) at scales larger than 333km. Then, because the resolution of future GOCE data are expected to be of 100 km, we simulated a “GOCE” MDT filtering the SMDT scales shorter than 100 km. As presented before, these two MDT are used for comparing absolute velocities, obtained by adding derived altimetric velocities, to buoy’s velocities. The results, displayed in Table 1, allow to highlight two major points: first, the present resolution of GRACE geoids is still too coarse to correctly estimate the MDT of the Mediterranean Sea. Better comparison results to observations are obtained using no mean at all (0-mean column of Table 1). Second, even a high resolution geoid as GOCE will not allow to retrieve the shortest scales of the Mediterranean MDT. Better comparison results are obtained with the SMDT, meaning that the scales shorter than 100 km contained in the SMDT are significant.



SMDT First guess 0-mean MDT“GOCE” MDT GRACE

RMSU (cm/s) 9.6 10.4 10.9 10.4 13.6

RMSV (cm/s) 11.5 12.6 13.5 12.1 13.8

Regression slope 0.45 0.34 0.15 0.30 0.19 Table I

Comparison results: Root Mean Square differences (RMS) in cm/s and Regression slope (Rs) between in situ drifting buoy velocities and the corresponding absolute altimetric velocities obtained adding to the altimetric anomalies different MDT

solutions.

Conclusion

An estimation of the Synthetic Mean Dynamic Topography MED-RIO05 (and the corresponding mean geostrophic circulation) has been obtained for the 1993-1999 period, by correcting the mean deduced from the MFSTEP model with drifting buoy velocity and altimetric data combined using the synthetic method. In addition to be a crucial issue for assimilating absolute altimetric data into operational forecasting systems like MERCATOR or MFSTEP, the accurate estimate of the Mediterranean MDT from in situ measurements and altimetric data as described here offers a unique opportunity for the validation of future GOCE data. Furthermore, it will be complementary to the MDT directly deduced from GOCE geoid whose limits will be precisely be reach in areas like the Mediterranean Sea, where mean spatial scales are expected to be less than 100 km.

The MDT in the Mediterranean Sea in the MERSEA project

By Laurence Crosnier

MDT differences between MFS, FOAM, MERCATOR and MED-RIO05

Within the MERSEA project, three ocean forecasting systems are releasing analysis and forecast of the ocean state in the Mediterranean Sea. They are the Italian MFS system, the English FOAM system and the French MERCATOR system. Figure 5 shows the various MDT used as a reference for sea surface height anomalies data assimilation in the MERCATOR, MFS and FOAM systems as well as the MED-RIO05 SMDT described in section 3. The basin average has been subtracted to all the MDT in order for them to be comparable. Globally, the MDT from MFS, MERCATOR and MED-RIO05 show a realistic circulation, with nevertheless a stronger north-south gradient in MFS and MED-RIO05 than in MERCATOR. The FOAM does not show a realistic circulation in the Mediterranean Sea and will not be discussed further. The main differences between the MERCATOR, MFS and MED-RIO05 MDTs are located in the following areas:

• In the Alboran Sea, the Anticyclonic Western and Eastern Alboran Gyre (WAG and EAG), as well as the Almeria Oran Jet (AOJ) are not exactly located at the same place in the MERCATOR, MFS and MED-RIO05. MED-RIO05 SMDT shows, in-between the WAG and EAG, a stronger cyclonic structure centered at 3˚E 36˚N, which could be a realistic signature of a cyclonic eddy formed in this area.

• The Algerian Current (AC) is located further away from the coast in MFS than in MERCATOR and MED-RIO05. Moreover, the AC is meandering in MFS whereas it has a more linear trajectory in MERCATOR. The AC in MED-RIO05 is trapped along the coast as in MERCATOR, but is stronger and meanders.

• In the Ligurian-Provencal Sea, the cyclonic Gulf of Lion Gyre (GLG) is 8cm stronger in MFS and MED-RIO05 (which uses MFS as a first guess) than MERCATOR.

• In the Tyrrhenian Sea, the MED-RIO05 SMDT shows the Bonifacio Gyre (BG), which is as well in MERCATOR but with a weaker intensity. The BG is not in MFS.

• MED-RIO05 SMDT shows a strong cyclonic structure in the area to the South and South-East of Sardinia, which you do not find in MFS and MERCATOR.

• In the South of Sicily, the gradient associated with the AIS (Atlantic Ionian Stream) is stronger in MED-RIO05 than in MERCATOR and MFS, with more pronounced structures of the ABV (Adventure Bank Vortex), MCC (Maltese Channel Crest), ISV (Ionian Shelfbreak Vortex) and MRV (Messina Rise Vortex).

• In the middle of the Ionian Sea, we notice a 10cm difference located at 17.5 ْ E-34.5 ْ N between MFS and MERCATOR, due to an anticyclonic eddy in MFS, not appearing in MERCATOR and MED-RIO05. In this area, MED-RIO05 shows a large anticyclonic structure, stronger than in MFS and MERCATOR. To the South along



the Lebanese coast, MERCATOR and MFS show a continuous current trapped along the coast. MED-RIO05 does not show such a current.

• In the Adriatic Sea, we notice three realistic cyclones in MED-RIO05. MFS and MERCATOR show the two most southern structures, the one to the south being stronger in MFS.

• In the south of Crete, we notice that the Western Cretan Cyclone (WCC) is 8cm stronger in MFS and MED-RIO05 than in MERCATOR. None of the MERCATOR and MFS MDT shows the Ierapetra Anticyclone (IA), the Mersa Matruh Anticyclone (MMA) and the Shikmona Anticyclone (SA), which are resolved by MED-RIO05. Nevertheless, the Rhodes Gyre (RG) is well captured by MERCATOR, MFS and MED-RIO05.

• In the south of the Levantine basin along the Egyptian coast at 28 ْ E, MERCATOR shows a weak current trapped along the coast. MFS has a stronger current which meanders. MED-RIO05 shows an even stronger current, which also meanders.

• In the south-East of Cyprus and along the Lebanese coast, we find the Asia Minor current, stronger and further away from the coast in MFS than MERCATOR. MED-RIO05 shows, as in MERCATOR, a current trapped along the coast, but stronger than in MERCATOR.

Figure 5

MDT (in meters) in MERCATOR (top left), MFS (top right), FOAM (bottom left) and MED-RIO05 (bottom right)

MDT impact on the Gulf of Lion winter convection

In this section, we will look at the influence of the Gulf of Lion Gyre MDT amplitude on winter convection. First, we notice that there is no convection (Figure 6) in the center of the Gulf of Lion Gyre (GLG) in MERCATOR during the 2004 and 2005 winters (as well as in 2002, see newsletter numéro 11, article from K. Béranger) (lien a mettre), whereas convection is taking place in MFS.

A salinity section in February 2004 at 5.5°E (not shown) shows a convection plume from surface to bottom at 42°N-5°W in MFS, whereas salinity stratification has not been eroded in MERCATOR.

Western Mediterranean Deep Water (WMDW) formation in the GLG has been studied extensively by the (Medoc Group, 1970). WMDW formation has been characterized by 3 different phases:.



Figure 6

Hovmuller diagram showing the maximum of the mixed layer depth (in meters) in the Gulf of Lion as a function of time (from June 2003 till January 2005) and depth

1. A preconditioning phase, during which the presence of a cyclonic gyre in the center of the Gulf of Lion reduces the stability of the surface layer (a distinct doming of isopycnals is present near 42 ْN - 5 ْW).

2. A violent mixing phase, triggered by the onset of the Mistral (dry continental north-westerly winds blowing from the Rhone river valley). The convection takes place in the center of the gyre. The dense surface water mixes with the saltier but warmer subsurface water.

3. Finally, a sinking/spreading phase. The newly formed WMDM leaves the formation area, while water advected by the Ligurian-Provencal Current replaces the surface and intermediate waters and again, a three layer stratification is re-formed.

We noticed earlier that the GLG amplitude is weaker in the MERCATOR MDT than in the MFS one. Furthermore, we notice on Figure 7 (middle panel) that the annual mean salinity across the 5.5°E vertical section shows isohalines in a doming shape at the surface at 42.2°N-5.5°W, as in the MEDATLAS climatology (Figure 7, right panel). The same salinity section in MERCATOR (Figure 7, left panel) shows a smaller and deeper doming, with isohalines that do not outcrop. The more pronounced doming in MFS than MERCATOR is most likely related to stronger cyclonic circulation in the MDT from MFS than MERCATOR..

Figure 7 Annual mean (June 2003- June 2004) salinity vertical section as a function of latitude and log10(depth) at 5.5� E in

MERCATOR (left), MFS (middle) and MEDATLAS (right).

The total heat loss (the heat flux term includes the heat flux through the relaxation terms) during winter 2004 is larger by 80W/m2 in MFS than MERCATOR in the GLG area (Figure 8). Again, this is related to the appearance of convection in MFS and not in MERCATOR. The wind stress amplitude in the GLG during winter 2004 is the same in MERCATOR and MFS (not shown).



Figure 8

Total Net Heat flux (including relaxation terms) in W/m2 during winter 2004 (January to March) in MFS (left) and MERCATOR (right)

In conclusion, the MDT differences in the GLG area between MFS and MERCATOR can explain partly the absence of convection in MERCATOR during the 2004 and 2005 winters. First, the stronger MFS MDT GLG gathers the isotherms and isohalines in a doming shape at the center of the gyre. The stratification brought closer to the surface is thus more likely to get eroded by the Mistral wind and convection is more likely to happen in MFS than in MERCATOR. Second, the surface heat flux loss during the 2004 winter is larger in MFS in the GLG than in MERCATOR. This is going along with the convection event in MFS and with the fact that MFS uses a Bulk formulae heat flux formulation (MERCATOR does not use such a formulation). When convection starts, a mixing of the subsurface warmer waters with the cold surface waters occurs. The warmer surface waters release their heat to the atmosphere. Such a heat loss goes on as long as the ocean is warmer than the atmosphere thanks to the Bulk Formulae flux formulation in MFS. In other words, in MFS, the cold Mistral wind blowing over the GLG is eroding the surface ocean stratification from the doming shape, triggering convection in the middle of the gyre as well as a heat flux loss (through latent and sensible heat fluxes) from the ocean to the atmosphere. Finally, the temperature from an observed XBT section across the Gulf of Lion on February 22 2005 (Figure 9) shows a rather homogeneous water column from the surface to 900 meters depth at 42°N, whereas the same section in the MERCATOR model shows a too stratified water column. The same section in the MFS model (not shown) shows a homogeneous water column similar to the observations, suggesting that the convection process in MFS is more realistic than in MERCATOR.

Figure 9 Left Panel : Observed In situ temperature (CORIOLIS) along a XBT track across the Gulf of Lion on February 22 2005. Right

Panel : Potential temperature in the MERCATOR model across the same section at the same date.

Conclusion

Three different consecutive studies perform about the MDT in the Mediterranean Sea were presented here. First, due to the necessity of a mean field for assimilating altimetric SLA in the MERCATOR PSY2v1 prototype, a MDT from model simulations was derived by E. Greiner and F. Hernandez. More recently the Synthetic MED-RIO05 MDT has been estimated by M.-H. Rio. And then, the comparison study between Mediterranean forecasting systems (in the framework of the E.U. MERSEA project), showed that the MERCATOR MDT seems to have a too weak cyclonic gyre in the Gulf of Lion, preventing winter convection from happening. MFS and MED-RIO05 (which uses MFS MDT as a first guess) present a stronger cyclonic gyre. Model comparison to observations (XBT section) suggested that the convection process in MFS is more realistic than in MERCATOR.



Moreover, the MERCATOR team might switch and use in the near future the MED-RIO05 SMDT in the Mediterranean basin. The question remains open whether all the non permanent structures as the the Ierapetra Anticyclone (IA), the Mersa Matruh Anticyclone (MMA) and the Shikmona Anticyclone (SA) which are present in the 1993-1999 MED-RIO05 SMDT, would perturbate the model physics.

References

Bretherton, F.P., R.E. Davis, et C.B. Fandry, 1976: A technique for objective analysis and design of oceanographic experiments applied to MODE-73, Deep-Sea Research, 23, 559-582.

Demirov, E., N. Pinardi, C. Fratianni, M. Tonani, L. Giacomelli, et P. De Mey, 2003: Assimilation scheme of the Mediterranean Forecasting System: operational implementation, Ann. Geophysicae, 21, 189-204.

Hernandez, F., et P. Schaeffer, 2001: The CLS01 Mean Sea Surface: A validation with the GSFC00.1 surface. Rapport n°, édité par CLS, Ramonville St Agne. pp. 14.

Le Traon, P.-Y., et F. Hernandez, 1992: Mapping of the oceanic mesoscale circulation: validation of satellite altimetry using surface drifters, Journal of Atmospheric and Oceanic Technology, 9, 687-698.

Lee, C., 2004: Multi-disciplinary perspectives on a wintertime bora wind event-Intensive studies of the Northern Adriatic, EOS, in preparation.

Mauerhan, T.A., 2000: Drifter Observations of the Mediterranean Sea Surface Circulation, pp. 111. Thèse de: Master of Science in Physical Oceanography. Naval Postgraduate school, Monterey, CA.

Mauri, E., et P.-M. Poulain, 2004: Wind-driven currents in Mediterranean drifter data. Rapport n° OGS Tech. Report 1/2004 - OGA-1, édité par OGS, Trieste, Italy. pp. 25.

Medoc group, 1970: Observation of formation of deep water in the Mediterranean Sea, Nature, 227, 1037-1040.

Poulain, P.-M., R. Barbanti, R. Cecco, C. Fayos, E. Mauri, L. Ursella, et P. Zanasca, 2004: Mediterranean surface drifter database: 2 June 1986 to 11 November 1999, dans CD-ROM, édité, OGS,Trieste, Italy.

Rio, M.-H., et F. Hernandez, 2004: A Mean Dynamic Topography computed over the world ocean from altimetry, in situ measurements and a geoid model, Journal of Geophysical Research, 109 (C12), C12032 1-19 - doi10.1029/2003JC002226.

Rio, M.-H., P.-M. Poulain, A. Pascual, E. Mauri, G. Larnicol, et R. Santoleri, 2005: A Mean Dynamic Topography of the Mediterranean Sea computed from altimetric data and in situ measurements., Journal of Marine Systems (accepted).


ARGO network for an oceanic synthesis of circulation in the North Atlantic using a 4D-variational method

Potential of the ARGO network to produce an oceanic synthesis of the hydrology and the circulation in the North Atlantic using a 4D-variational method By Gaël Forget, Bruno Ferron and Herlé Mercier

Introduction

The ARGO network is composed of drifting floats that provide temperature and salinity profiles from 2000 m up to the surface every 10 days. With a final objective of 3° average horizontal resolution over all the oceans (more than half are already deployed), this network will provide a quantity of information on the heat and salt content variability never reached before. Floats also provide valuable information on water mass motions at their drifting depth.

The objective of this work is to examine the potential we may expect from an assimilation of vertical profiles of temperature and salinity of ARGO floats to produce analyses of the oceanic hydrology and circulation. Given the Lagrangian character of the observations and their inhomogeneous space distributions, the 4D-variational (4D-var) assimilation is a suitable method to produce a data synthesis. Indeed, this method propagates the information contained in the observations following the model dynamics (advection and waves) and it takes into account the observations at their exact date and position. The method and the observations used here will be used by Mercator in the future.

Direct and adjoint models

We use a general circulation model (Marshall et al.1997ab) of low resolution (1° on the horizontal and 23 vertical levels). The exercise is carried out on a North Atlantic configuration that extends from 20°S to 70°N. The model uses a no-slip lateral boundary condition, a convective adjustment in case of hydrostatic instabilities, a vertical turbulent viscosity (resp. horizontal) of 10-3m2s-1 (resp. 2x104m2s-1), a vertical turbulent diffusivity (resp. horizontal) of 10-5m2s-1 (resp. 103m2s-1).

The model is forced by the ERA15 ECMWF reanalyses. Temperature and salinity at the sea surface and along the artificially closed boundaries are restored towards the Reynaud et al. (1998)'s climatology. The model is spun-up from rest over the period 1979 to 1991. Tangent-linear and adjoint models are produced by the Tangent linear and Adjoint Model Compiler (TAMC; Giering and Kaminski, 1998). They are used by the assimilation algorithm. The adjoint code of the model thus produced is checked each time a modification is made in a subroutine of the direct model. We used the non incremental 4Dvar assimilation technique. The assimilation window is one year length.

Method

In order to objectively quantify the constraint that the hydrology of the ARGO network produces for the reconstruction of the oceanic state of the ocean using a 4D-var assimilation, a bunch of twin experiments were carried out. These experiments consisted in:

• choosing arbitrarily a one year period (here summer 1987 to summer 1988) of the model spin-up as the oceanic state of reference; the summer 1987 represents the reference initial conditions in temperature Tr and salinity Sr.

• simulating a network of Lagrangian floats that produce synthetic profiles of temperature and salinity every 10 days in the oceanic state of reference.

• Taking as a first guess temperature Tb and salinity Sb the summer 1989 fields. Tb and Sb correspond to our a priori knowledge of the summer 1987 hydrology; Although the forcings remain the same as those used to produce the oceanic state of reference, the one-year model integration from Tb and Sb produces a really different oceanic state from the reference one (See the differences (Tb-Tr) and (Sb-Sr) in Fig 2a and 3a).

• adding to the synthetic profiles a Gaussian noise which amplitude is as large as the differences (Tb-Tr) and (Sb-Sr); this noise is uncorrelated in space and time and simulates the instrumental error and the representativity error that real observations would have.

• assimilating the synthetic profiles over the one year period in order to find optimal summer 1987 initial conditions in temperature and salinity (control variables) that produce the best estimate of the reference oceanic state. Tb and Sb are the first guess of the optimal initial conditions.



The assimilation proceeds iteratively. For a given iteration i, the integration of the full direct model starting from the initial state (Ti, Si) provides a cost function Jo(i) that sums the quadratic misfits of the model to the observations weighted by a diagonal error covariance matrix R. The Jo(i) minimisation constrains the model trajectory. A cost function Jb(i) is added to Jo(i) that sums the quadratic deviations of the initial state (Ti, Si) to the background (Tb, Sb) weighted by a diagonal error covariance matrix B. The Jb(i) minimisation contrains the initial condition to stay close to the first guess. The reverse integration with the adjoint model and the use of a descent algorithm provide new initial conditions in temperature and salinity used as starting point for the iteration i+1 such that: Jo(i+1)+Jb(i+1)<Jo(i)+Jb(i). When the difference in cost function between two consecutive iterations becomes sufficiently small, the iterative process is stopped and the last iteration represents the optimal initial conditions. The matrix R is calculated for each model vertical level from the median value of the standard deviations of Reynaud et al. (1998)'s climatology. The matrix B is equal to the inverse of the variances associated to (Tb-Tr) and (Sb-Sr) at each vertical level.

When the intensity of the velocity field at the drift depth is low, a float samples several times the same water column before being advected to the next grid point (almost 12 profiles available for a model grid of 100km length when the drift velocity is 1 cm/s). In this case, a simple average of the repeated profiles would filter the random noise. When the dynamics is faster, a float will be able to cross a grid point without making any measurement. In this case, the 4D-variational method shows all its importance to filter the noise. Indeed, it transmits the information contained in the measurements following the physics of the model, which allows correlations between profiles that obey to the same dynamics (advection and waves). The longer the assimilation window length, the further the information contained in measurements is carried away from its origin, and the larger the scales of the initial state changes. In order to take advantage of these properties, data are assimilated over a one-year long window length. On one hand, a longer window length requires taking the model error (e.g. forcings) into account. On the other hand, a shorter window length decreases both the propagation of the information through the adjoint but also the number of observations. A shorter window length has a cost function more quadratic what facilitates the search of the optimal initial state.

Results

We now illustrate some results obtained when using a network of 300 floats that corresponds to the ARGO spatial density on the North Atlantic and a drifting depth of 800m (fig. 1). The first guess is composed of summer 1989 temperature and salinity fields.

Figure 1 Positions and a number (grey scale) of float profiles available per grid point over the period summer 1987-summer 1988.

Month 12 of the model integration starting from the first guess and with the summer 1987-summer 1988 surface forcing fields, show large scale differences in temperature at 847m with the state of reference. Differences locally exceed 1°C north of 10°N (Fig. 2a). Further south as well as in the Norwegian Sea, differences are much weaker because of restoring conditions along the artificially closed boundaries. After profiles of temperature and salinity are assimilated, most of the initial temperature differences are reduced by a factor of 2 to 5 (fig. 2b). This is also verified for the previous months. However, in some regions, the initial temperature differences remain high since no information is available to constrain the temperature. The salinity behaves similarly to the temperature.



Hydrology analyses are sensitive to the number of floats. A reduction by 3 of the float density (i.e. 100 floats) increases the number of un-sampled regions and produces residuals often larger than 0.4°C in the open ocean whereas a increase by 3 (900 floats) leads to residuals everywhere lower than 0.2°C except in a few coastal areas (fig. 2cd).

Figure 2 Differences in temperature at 847m for month 12 of model integration between the first guess or an analysis (solution after assimilation) and the state of reference. The analysis is produced by an assimilation of profiles of temperature and salinity generated by lagrangian floats in the state of reference. Differences are calculated with: a) the first guess, b) the analysis

produced with 300 floats (typical horizontal resolution of ARGO for the North Atlantic), c) the analysis produced with 100 floats, d) the analysis produced with 900 floats. The contour interval for the positive values is 0.2°C.

Although the assimilated profiles directly constrain the density field, velocities are also modified during the model integration of the optimal initial state by adjustment to the density field. Thus, one can legitimately expect from the assimilation a constraint on the model circulation. At month 12 of model integration, the differences in meridional overturning cell between the first guess and the state of reference show increased transports in the deep branch (below 1500m) between 10°S and 40°N and a decrease north of 50°N (fig. 3a). Closer to the surface, only the region from equator to 15°N shows significant differences. After assimilation, the initial differences in meridional overturning are divided by a factor 2 to 4 (fig. 3b). In these twin experiments, the meridional overturning cell is constrained by the data collected over one year from the 300 floats. The barotropic circulation is constrained similarly (not shown).



Figure 3 Difference in meridional overturning (units 106m3s-1) for month 12 of model integration between the oceanic state of reference

and a) the first guess, b) the analysis produced with 300 floats.

Conclusion

In twin experiments, the true oceanic state is known and errors on observations as well in model (null in this study) are controlled. Thus, it is possible to diagnose objectively the quality of the analyses produced by the assimilation at each grid point and time step. This type of diagnostic in the case of real data assimilation can be calculated only where and when independent observations (i.e. not assimilated) are available.

With a one year assimilation window length, our twin experiments show that temperature and salinity profiles of an ARGO-type float network constrain the stratification and the circulation of a low resolution model. This constraint acts even if the noise on observations has an amplitude as large as the difference between the first guess and the true solution. Thus, the assimilation manages to filter the noise efficiently.

A major difference between our twin experiments and real data assimilation experiments comes from the existence of model error. Although perfectible (error covariance matrices are diagonal in this study), the application of our assimilation scheme to real observations still show a strong constraint of the stratification and circulation if we compare the analyses to independent observations (Forget 2005)

References

Forget, G. 2005 : Profils ARGO et assimilation 4Dvar pour le suivi climatique de l’océan Nord Atlantique, Thèse de l’Université de Bretagne Occidentale, 140pp.

Giering, R., and T. Kaminski, 1998: Recipes for adjoint code construction. ACM Trans. Math., 24, 437-474.

Marshall, J., C. Hill, L. Perelman, and A. Adcroft, 1997a: Hydrostatic, quasi-hydrostatic, and non-hydrostatic ocean modeling. J. Geophys. Res., 102, 5733-5752.

Marshall, J., A. Adcroft, C. Hill, L. Perelman, and C. Heisey, 1997b: A finite-volume, incompressible Navier-Stokes model for studies of the ocean on parallel computers, J. Geophys. Res., 102, 5753-5766.

Reynaud, T., P. Legrand, H. Mercier, and B. Barnier: A new analysis of hydrographic data in the Atlantic and its application to an inverse modelling study, Intern. WOCE News., 32, 29-31

a b


An observing system simulation experiment for SMOS: presentation of the study

An observing system simulation experiment for SMOS: presentation of the study By Florence Birol, Pierre Brasseur, Lionel Renault, Charles-Emmanuel Testut and Benoit Tranchant.

Introduction

Two parameters determine the ocean density at a given pressure: temperature and salinity. It is well known that temperature has important effects in determining the ocean circulation and thereby the climate system. Recent studies suggest that salinity is not only a tracer of the ocean variability but also plays an active role on the ocean dynamics: although salinity has little direct effect on the atmosphere, its variability affects the circulation through density effects (Cooper, 1988; Roemmich et al., 1994). In the North Atlantic, sea surface salinity (SSS) modulates the intensity of deep convection and then the thermohaline circulation. In tropical oceans, salinity impacts vertical stratification, and can thus favour or inhibit vertical mixing. Vertical distribution of salinity also impacts the surface layer momentum budget, by affecting mixed layer depth and modulating the response to wind forcing.

It is therefore important for any ocean prediction system to represent this field with a reasonable accuracy. Unfortunately, a well-known deficiency of ocean models is their tendency to produce spurious drifts in salinity fields. This is mainly due to errors in freshwater and heat fluxes. Through mixing, errors in the properties acquired at the ocean surface are then transferred to subsurface and deep layers (Paiva and Chassignet, 2001). Note also that inaccurate initial conditions and model deficiencies (advection, mixing, or entrainment terms, but also a too low resolution) contribute also largely to the salinity error budget. As a result, the vertical salinity error structure is complex.

Context of the study

The MERSEA intercomparison exercise has shown that problems in the surface water masses representation is a common feature of different ocean forecasting systems: MERCATOR, FOAM, TOPAZ and MFSTEP (L. Crosnier, personal communication). Assimilation of salinity observations would help operational systems to better represent water masses. Unfortunately, unlike other ocean parameters (as sea level anomalies or sea surface temperature), salinity has been sparsely measured at sea, limited mostly to summertime observations or commercial shipping lanes. However, over the last decade, it has been demonstrated that SSS can be measured from space and two new missions will allow to map sea surface salinity in the near future: the European Space Agency’s SMOS (Soil Moisture and Ocean Salinity, planned for 2007) mission and the proposed US/Argentinean AQUARIUS mission (planned for 2008). The main characteristics of these two missions are summarised in Table 1.

SMOS Aquarius

Scientific objectives Soil moisture and SSS SSS

Measurements goals Accuracy of 0.1 psu for a 10-30 days average and for an area of 200x200 km

Accuracy of 0.2 psu for a monthly average and for an area of 100x100 km.

Mission characteristics Global coverage every 3 days and ~45 km resolution.

Global coverage every 8 days and ~60 km resolution.

Table I SMOS and Aquarius mission main characteristics

A major difficulty affecting each observing system is the definition of a suitable observational network. In the context of SMOS and Aquarius, the product resolution will be strongly constrained by technical difficulties: because of the complexity of SSS retrieval, the measurement accuracy for a single pixel will be around 0.5-1.5 psu. The SMOS products accuracy requirement is specified as 0.1 psu for a 10 days and 2 degree by 2 degree resolution. It has been shown that this level of accuracy can be obtained only by spatial and temporal averaging of the measurements. An important question we need to address is then: What is the best strategy to optimally use the future SMOS and Aquarius data in the context of ocean prediction systems, from the perspective of monitoring the mesoscale ocean circulation? One efficient way to address this question is by conducting Observing System Simulation Experiments (OSSE).



Objective and methodology

Since 2004, the Mercator assimilation team is involved in the ESA’s study of key concepts concerning the future operational use of the satellite derived SSS products. The main objective of this new activity is to evaluate the difficulties in, and the benefit from, the assimilation of SSS data products, as expected from SMOS and AQUARIUS, for ocean prediction systems. Another motivation is to assess and compare the usefulness of the different data combination strategies (for SMOS/Aquarius).

The SMOS Observing System Simulation Experiment (OSSE) approach will consist of performing realistic numerical assimilation experiments with the aim of quantifying the contribution of future SSS products in addition to data which are already included in the Mercator assimilation system (SLA, SST, in situ profiles).

The approach consists of:

1. Simulating realistic pseudo-SSS products

2. Performing a simulation including the pseudo-SSS products in the assimilation system.

3. Assessing the contribution of the pseudo-SSS products through the comparison of different simulated fields with the fields obtained when pseudo-SSS products are not included in the assimilation system. This contribution will be analysed for both surface and subsurface layers and on a regional basis (MNATL configuration).

The approach adopted in this study is based on a review of state-of-the-art methods recently developed for SSS data assimilation. We summarise the main points of this review in the next section, while an extended description of the results of the SMOS OSSEs will be presented in a forthcoming Mercator Newsletter.

Approaches for sea surface salinity data assimilation Historically, the need for accurate salinity information to reconstruct the state of the ocean was pointed out by Cooper (1988) in the context of data assimilation. Many studies have shown the importance of salinity correction in ocean assimilation schemes. However, because of salinity data scarcity, very few projects assimilating this kind of observations have been undertaken so far. To our knowledge, the first impact study of observed SSS on a model performance was performed by Reynolds et al. (1998) based on a simple Newtonian relaxation method. This method only allows a control on SSS, and assumes physical adjustment. However, the results showed no adjustment at depth and that the control at the surface was impaired by the retroaction of the subsurface layers. Durand et al. (2002, 2003) have shown that the use of an advanced multivariate assimilation method is necessary to correctly control all the state variables (at the surface as well as in subsurface) related to SSS. Three-dimensional multivariate error covariances allows to extrapolate SSS information into 3D and multi-parameters information (Figure 1).

Ocean mixed layer

SSS information

3D and multi-parameters information

Figure 1 Simple scheme of the multivariate assimilation of SST and SSS data.

The benefit expected from the assimilation of SSS data products was also evaluated in the framework of the European TOPAZ project (TOPAZ, Scientific final report, 2003). Based on the SEEK filter (Singular Evolutive Extended Kalman filter; Pham et al., 1998), experiments were performed to examine the impact of the assimilation of SSS data on the North Atlantic Ocean state estimation. High resolution altimetric data and sea surface temperature (SST) were assimilated in addition to climatological SSS. The delicate issue with multivariate assimilation is that any misspecification in the multivariate error covariance may lead to inappropriate corrections of the forecast fields. This study has shown that the joint assimilation of SST and SSS is able to correct many deficiencies, even with a fairly large observation error variance. However, it requires to properly assimilate the low resolution SSS data to avoid spurious effect. In particular, it is important to well estimate the observation operator used in the



analysis scheme by computing the model equivalent (temporally and spatially) to the SSS data: in that way, the model mesoscale signal can not be influenced by SSS.

A general conclusion of recent studies is the importance of the assimilation of different data types, each bringing a specific contribution to the identification of the ocean state. It is demonstrated to be particularly useful in compensating in weakly specified cross-field error covariances. Typically, assimilation of altimetric sea level anomalies and in situ profiles is relevant to better control the subsurface layers, whereas SSS and SST data are intimately linked with the mixed layer behaviour. This last remark suggests to use an assimilation scheme able to take into account a high number of observations..

Conclusion

The OSSE presented here will be conducted using the new generation Mercator assimilation scheme (SAM2). SAM2 is particularly suitable for SSS assimilation: this scheme is multivariate, potentially allows dynamical error propagation and is able to take into account a high number of observations. Note that this scheme is based on the SEEK filter which has already been used to assimilate pseudo-SSS data (Durand, 2003; the TOPAZ project). The first step of this study has consisted to update SAM2 to assimilate SSS in addition to conventional data and to validate this new system.

The assimilation of SSS data also assumes the introduction of new information at low resolution in the system, requiring a specific parameterisation of the analysis scheme. In addition, we have also decided to introduce a representativeness error, estimated from the SSS data set, and which reflects the model capacity to represent the observed signal (Figure 2). The error shown on Figure 3 is estimated from the difference between the model and the Levitus climatological SSS and from physical considerations. Large errors occur where the model mesoscale signal is large: in the Gulf Stream and frontal regions.

Figure 2 Mean annual SSS distribution from Levitus (left) and associated representativeness error (right)

Finally, today, the Mercator system (SAM2) already assimilates different data types: altimetry, SST and in situ profiles. Consequently, SSS will enrich this list. This study is particularly important because an optimal assimilation of SSS observations should theoretically allow us to make a significant step toward more realism of, not only the simulated SSS field, but also all simulated mixed layer properties and then impacts many other applications, as ecosystems studies.

References

Durand F., G. Gourdeau, T. Delcroix and J. Verron, 2002. Assimilation of sea surface salinity in a tropical OGCM: a twin experiment approach. J. Geophys. Res., doi:10.1029/2001JC000849, 2002.

Durand, F., L. Gourdeau, T. Delcroix, and J. Verron, Can we improve the representation of modeled ocean mixed layer by assimilating surface-only satellite-derived data? A case study for the tropical Pacific during the 1997-1998 El Niño, J. Geophys. Res., 108(C6), 3200, doi:10.1029/2002JC001603, 2003.

Godfrey J.S. and E.J. Lindstrom : The heat budget of the equatorial western Pacific surface mixed layer. J. Geophys. Res., 94, 8007-8017, 1989.



Paiva A.M. and E.P. Chassignet: The impact of surface flux parameterisations on the modelling of the North Atlantic Ocean. J. Phys. Oceanogr., 31, 1860-1879, 2001.

Reynolds, R., M. Ji and A. Leetmaa: Use of salinity to improve ocean modeling, Physics and Chemistry of the Earth, 1998.

Pham D.T., J. Verron and M.C. Roubaud: A singular evolutive extended Kalman filter for data assimilation in oceanography, J. Marine System, 16, 323-340, 1998.

Roemmich D., M. Morris, W. Young and J.R. Donguy: Fresh equatorial jets. J. Phys. Oceanogr., 24, 540-558, 1994.

The ToPAZ group: TOPAZ final report, 2003.

- Notebook -

Editorial board

Nathalie Verbrugge

Secretary

Sophie Baudel

Articles

News : MERSEA : en route to European operational oceanography

Pierre-Yves Le Traon


Fabrice Hernandez, Marie-Hélène Rio, Laurence Crosnier

Potential of the ARGO network to produce an oceanic synthesis of the hydrology and the circulation in the North Atlantic using a 4D-variational method

Gaël Forget, Bruno Ferron, Herlé Mercier


Florence Birol, Pierre Brasseur, Lionel Renault, Charles-Emmanuel Testut, Benoît Tranchant

Contact

Please send us your comments to the following e-mail address: [email protected]

Next issue : July 2005

Mercator Ocean newsletter 17

Environment

Transcript of Mercator Ocean newsletter 17