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Activity report WP 7 Modelling and data assimilation Section 1 – Work Package objectives and major achievements during the reporting period The general objectives of WP7 are (i) to perform research and development on physical modelling, ecosystem modelling and data assimilation as required for the operational objectives of MERSEA during the whole life of the project, (ii) to provide all the necessary tools (physical model codes, biogeochemical model codes, data assimilation codes) that are required by the MERSEA project to reach its operational objectives, and (iii) to bring ad hoc scientific innovations into the project and to gather all the relevant European capabilities in order to make sure that operational systems are maintained at the most advanced level thus benefit from the progress achieved in the research community, during the project life-time and beyond. This work package is divided into three main components, dedicated to physical models, biogeochemical models and data assimilation methods : The main activities of the Physical models task (Task 7.1) are: Transfer the intermediate high resolution (1/4°) physical model code and insure relevant expertise on running the code, analysing and assessing the results at global scale. Realize R&D activities for continuous model improvements (numerical schemes, bottom boundary layer, tidal mixing, mixed layer,...). Contribute to the development of a global ORCA high resolution (1/12°) physical model. Develop and transfer the nesting capabilities (software and expertise) to make the one-way and two- way connections between the large scale model and the regional models. This first year of MERSEA activity is characterized by noticeables advances (partial steps, numerical scheme, …) in the development of the 1/4° resolution global model and the preparation of the first transfer for implementation. The main activities of the Biogeochemical models task (Task 7.2) are: Develop a biogeochemical model to be coupled to the MERSEA global physical model: nitrogen- based model with implicit representation of size classes in phytoplankton and sinking particles, mechanistic model of DOM production as a function of plankton size, mechanistic model of DON and DOC consumption with a minimum number of variables, inclusion of different limiting nutrients and carbon. To develop the prototype of a coupled physical/biological assimilative system covering the North Atlantic, which will be used to demonstrate the capacity to routinely estimate and forecast biogeochemical variables. This first year of MERSEA activity is characterized by considerable progress in the development of a new, implicitly size-structured ecosystem model, including first calibration studies at various time-series sites. In addition, a number of sensitivity studies have been performed with presently available ecosystem models coupled to OPA. These will form a reference basis for future assessments of improved ecosystem model components. During the first year of MERSEA, a beta-version of the platform has been developed using a rigid-lid OPA model of the North Atlantic at eddy-permitting resolution (MNATL), a sequential assimilation scheme (the SEEK filter) and a first-generation biogeochemical model (N1P1-type). The main activities of the Data Assimilation task (Task 7.3) are: Explore ensemble-based assimilation schemes (EnKF, SEEK, SIR) which will more rigorously tackle the statistical nature of the ocean forecasting problem (non gaussian error statistics, biased estimations, non-linear processes) 1 Improve the capacity of the integrated system to assimilate data from different sources (satellite altimetry, SST, SSS, in situ profiles) in the free-surface OPA circulation model This first year of MERSEA activity is characterized by significant advances with regard to the implementation of the SEEK filter into the free-surface OPA model and to important steps forward for the use of the SIRF into a biogeochemical model. Section 2 – Workpackage progress of the period Task 7.1. Physical Modelling Task objectives The main objective of the physical modelling subtask is to upgrade the numerical ocean general circulation model (GCM) NEMO1 which is one of the major components of the operational system developed in MERSEA. The starting point is the OPA8.1 ocean GCM developed at LODyC (Madec et al., 1998). The ultimate goal is to deliver to the project a global model at eddy-permitting resolution (1/4°), based on the NEMO code, an up-graded version of this model including a fully prognostic sea-ice model (the LIM model from the University of Louvain la Neuve, Fichefet et al., 1997), and recent parameterisation of topography (partial steps), an improved parameterisation of the mixing at the surface and in the interior, and most advanced numerical scheme. The model should also include grid-refinement capabilities, in relation with the need of WP10. A first version of the model, referred to as the DRAKKAR2 configuration ORCA-R025, will be delivered to the project at the end of the first 12 month period. A final version, validated by sensitivity experiments will be delivered at month 30. 1 NEMO: Nucleus for a European Model of the Ocean. 2 Because inherited from the DRAKKAR project. Scientific / Technical Progress made in this task Task 7.1.1 OPA HR model developments The task in on schedule. Progress made in the various subtasks are summarised below. Subtask 7.1.1.1. Partial steps and global 1/4° model configuration This subtask concerns principally the validation of the new numerical treatment of the bottom topography, and the implementation of the global 1/4° model configuration. Representation of the bathymetry by the introduction of partial steps. In a regular z-coordinate model (also referred to as a level model), the Full-Step (FS) representation of the bottom topography uses a vertical grid spacing made of pre-defined regular levels. For example, in a 46 level model as the one used by MERCATOR, levels below the main thermocline have a constant size of 250 m (Fig. 7.1.1.1a). Therefore, the bathymetry is represented by steps of 250 m height, and the total depth at a given point in the model, which is adjusted to the depth of the closest grid cell, is only an approximation of the observed depth. In the Partial Step (PS) representation of the bottom topography, the size of the grid cell closest to the bottom is adjusted to the observed depth of the ocean (Fig. 7.1.1.1b). Therefore, the size of the bottom grid cell is highly variable (from 25 m to 250 m in the model developed for MERSEA), which make the calculation of the pressure gradient more difficult. The great interest is a better representation of the real f/h contours, and a better communication between bottom cells, as shown in Fig. 7.1.1.1b. 2 Both FS and PS representation of bottom topography have been made available in NEMO, and implemented in the global 1/4° model. Full Step Partial Step Fig. 7.1.1.1. a) Full Step representation of bottom topography: the ocean total depth is adjusted to the depth of the closest grill cell. b) Partial Steps: the depth of the grill cell closest to the bottom is adjusted to the real ocean depth. Comparison of the impact of the partial step representation of topography in OPA8 with a model of the South Atlantic at eddy permitting resolution (1/3°). The Argentinian Basin is a region where interactions between bottom topography and mesoscale turbulence give rise to a very particular large scale circulation pattern, the Zapiola permanent anticyclone, a now well observed feature that only the SPEM model (a sigma coordinate model) was able to reproduce (De Miranda et al., 1999). In particular, regular FS z-coordinate models always failed to simulate the Zapiola Anticyclone. This area is thus very suitable to test new representation of the bottom topography at eddy permitting resolution in numerical models. We carried out at LEGI a series of model simulations with the Clipper 1/3° south Atlantic Model SATL3 to test the impact of the partial steps available in the modified version of OPA8. We remind here that this model uses the z-coordinate (42 vertical levels), and has three open boundaries at Drake passage (68°W), between South Africa and Antarctica (30°E) and at 16°S between Brazil and Angola. It is driven by daily climatology of air-sea fluxes derived from ECMWF re-analysis for the Clipper project. Two experiments have been carried out with SATL3, which differ only by the representation of the bottom topography: - Experiment S-AS1: the bottom topography is represented by the usual full cells (Fig. 7.1.1.1a). - Experiment S-AS2: the bottom topography is represented by the new partial cells (Fig. 7.1.1.1b). Both model configurations were integrated over a 10 year period. A detailed analysis and comparison of these two simulations is presented in a report from LEGI which evaluate the impact of partial steps to this configuration. Results show an very significant improvement of the simulated circulation in case of partial steps, in terms of dynamics and thermodynamics. In particular, compared to the full step simulation, the partial step solution shows: A more realistic circulation in the Confluence region, with a Malvinas Current reaching 40°S, and a good representation of the separation of the Brazil Current and a realistic split of the subtropical and subantarctic fronts. A signature of the Zapiola Anticyclone over the whole water column, with a simulated transport of the order of 80 to 100 Sv close to the value of 100 Sv deduced from observations. To our knowledge, this strong circulation feature was never reproduced by any full-step z-coordinate model. A "C-shape" distribution of the eddy kinetic energy (eke) in the western south Atlantic (Fig. 7.1.1.2) in better agreement with satellite estimates deduced from T/P and ERS. An improved distribution of water masses which now keeps to the large scale water mass distribution of revealed by the WOCE sections. 3 Full Step topography Partial Step topography Fig. 7.1.1.2: Eddy Kinetic Energy (eke) in the Argentinian Basin. Yellow indicates values greater than 500 m 2s2. The partial steps solution shows a C-shape distribution which agrees well with satellite observations of T/P and ERS. This improvement is due to a remarkable improvement of the mean currents and of the effect of topography on mesoscale turbulence. Our conclusions are thus very favourable to the use of Partial Steps in future applications of NEMO (the new version of OPA code) in MERSEA. However, the validation and understanding of the Partial Cells presented here is only preliminary and additional investigations of the impact of this representation of the bottom topography in eddy resolving models, and in the global 1/4° model in particular, is still necessary. A North Atlantic 1/4° prototype A first prototype of a North Atlantic regional model at an eddy permitting resolution of 1/4° resolution has been implemented with NEMO (referred to NATL4) to be used to assess the impact of the partial steps representation of topography in the North Atlantic. NATL4 is in fact a sub-domain of the global configuration ORCA -R025 (Fig. 7.1.1.3), using the same grid, bathymetry and forcing. NEMO includes the new version of OPA9 as ocean model coupled with the LIM sea-ice model. The development required a great number of tests. We used this configuration to test the representation of topography by using the Partial Steps against the usual but less accurate Full Steps. Experiments confirmed the incompatibility between the vorticity advection scheme and the partial steps topography, with the development of spurious barotropic circulation cells. A new vorticity advection scheme has thus been implemented, which solves that problem. Preliminary experiments show that the new advection scheme produces, by itself a significant improvement of the representation of all boundary currents. Figure 7.1.1.3: Model domain and bathymetry of NATL4 configuration. 4 New numerical scheme for momentum advection A new numerical scheme to treat the vorticity term in the momentum equation when the partial step configuration is used has been implemented and tested in NEMO. This scheme conserves energy and enstrophy. It results in a better representation of the dynamics, and a considerable reduction of the variance on the vertical velocity at the bottom. The global 1/4° configuration In this first year, we developed the 1/4 ° global DRAKKAR configuration ORCA-R025 and we carried out sensitivity experiments to confirm the choices made about parameterisation and numerical scheme after experiments carried out with South Atlantic (SATL3) and North Atlantic (NATL4) configurations. This large configuration counts 1442x1021x46 grid points on an ORCA-type 3 polar grid (Fig. 7.1.1.4). Grid, topography, masking and initial conditions are inherited from the POG global configuration of Mercator-Ocean. The implementation on massively parallel machines has been carried out at the IDRIS Centre on the IBM Computer IBM (186 proc.). Taking into account the scientific constraints of the project (in particular the use "partial steps"), and the technical constraints (computing time), it was essential to work with the new version of the code NEMO. Parallelisation required a significant work on the sea-ice model and the implementation of specific procedures of exchange between the processors located in the folding zone of imposed by the two geometrical poles of the grid in the northern hemisphere. Intensive testing of the MPI version of the code, with the partials steps and sea-ice produced exactly reproducible experiments, independently of the way the cutting onto processors is done. Test of the ORCA-R025 configuration have Fig. 7.1.1.4: Polar sight of the northern hemisphere of the been made on 186 processors (Fig.7.1.1.5). In computational grid of ORCA-R025 (resolution 1/4°, 1 point out of 20 is represented). Making the code parallel implied to develop terms of performance, the time step is 1440 s specific procedures of exchange between the processors located in (60 time steps per day), and one year of model the folding zone of imposed by the two geometrical poles of the simulation requires 2200h CPU on 186 grid. processors, and take about 12 h of elapsed time. 5 Fig. 7.1.1.5. Decomposition on 186 IBM processors of the DRAKKAR global 1/4° ocean circulation model. Colours indicate the ocean depth. Boxes represent the domain account for by a processors. Crossed boxes are 'land processors' not retained in the calculation. Numbers in abcissa and ordinate indicate model grid points. Experiments with the global 1/4° ORCA-R025 model A series of seven 10-years experiments have been run at LEGI. All experiments use the same climatological forcing . All experiments have in common a laplacian lateral isopycnal diffusion on tracers (300 m2s-1), an horizontal biharmonic viscosity for momentum (-1.5 1011 m4s-1). In the equatorial wave guide, an extra laplacian viscosity ( 500 m2s-1) is applied on the first three levels. The analysis of these experiments, as well as additional experiments, will be carried out in the next 12 month period for validation and tuning of this global configuration. Early conclusions of the experiments already carried out are listed below. - The new vorticity scheme in momentum equation represent the most drastic improvement compared to the previous era, in particular on the western boudary current systems. - The use of partial steps represents another undeniable improvement with regards to full steps representation of the bottom topography. In the Atlantic, this is particularly clear in the Argentine basin and the representation of the permanent Zapiola eddy, and in the "North West Corner" off Flemish Cap (see Fig. 7.1.1.6) 6 Fig. 7.1.1.6. Simulation G22 with the DRAKKAR configuration ORCA-R025 (with partial step, new vorticity scheme): Time mean (year 8 to 10) of the sea surface height (ssh, in meters). Contour lines at hogh latitudes represent the sea-ice concentration. With regards to full steps representation of the bottom topography, this solution represents a remarkable improvement of the dynamical circulation features. In the Atlantic, this is particularly clear in the Argentine basin (where the Zapiola eddy is clearly marked) and in the "North West Corner". The present global 1/.4° DRAKKAR configuration ORCA-R025 already represents a significant improvement compared to other state of the art model at that resolution. Although this model will receive, as planned, a thorough validation in the next 12 month period, in particular with the addition of the parameterisations developed within the subtasks 7.112 and 7.113, it is now ready for delivery to MERCATOR on schedule (D.7.1.1). Subtask 7.1.1.2. Tidal mixing A parameterisation of the effect of tides on the vertical mixing has been developed and introduced in the new version of OPA code, name NEMO. It is based on the idea of Simmons et al. (2004). It uses as input data the amount of energy dissipated by barotropic tides into internal waves provided by Lyard and Le Provost (2002) global ocean tidal model. A first test has been made at low resolution in ORCA2-LIM (2° resolution). It shows a realistic geographical distribution of the vertical diffusivity (Fig. 7.1.1.7). The effects on the global properties of the circulation of the world ocean are a significant improvement of the simulated thermohaline structure and of the meridional overturning circulation (Fig. 7.1.1.8). 7 Fig. 7.1.1.7. Vertical average of the tidally induced vertical eddy diffusivity coefficient. Fig.7.1.1.8. Global effective meridional overturning stream-function at the equilibrium (i.e. after 1,500 years). Upper panel: with a constant vertical diffusion of 0.1 cm/s in and below the thermocline. Lower panel: with a the addition of the tidally driven vertical mixing. Subtask 7.1.1.3. Improved mixed layer A 300 levels version of ORCA2lim 2° and of ORCA-r05lim 1/2° have been developed to assess the behaviour of the TKE scheme with a O(1m) vertical resolution in the top 300 meters. The analysis of the 10 yearlong simulations using the two resolutions is underway. A 1D vertical model using as much as possible the modules of the NEMO code has been developed and tested. It provides the framework for all the further development and tests of new vertical physics that will be undertaken in the following months. Subtask 7.1.1.4. Contribution to the development of the global 1/4° model 8 This task is planned to start at month 18. However, a first prototype of a global ocean model at an eddy permitting resolution of 1/4° with a fully prognostic sea ice model is being implemented (referred to ORCA-R025, see section 7.1.1.1) on a massively parallel computer under MPI. It uses the new version of the OPA code, named NEMO, fully written in Fortran 90. It can be used with full steps or with partial steps. An automatic domain decomposition software has been especially developed for the ORCA grid. Subtask 7.1.1.5 Contribution to the development of the 1/12° model This task is planned to start at month 18. Task 7.1.2 Nesting methods and Agrif package The task in on schedule. Progress made in the various subtasks are summarised below. Subtask 7.1.2.1: Synthesis of the needs and comments expressed by the users of the AGRIF package in WP10. The AGRIF tool has been (or is currently being) integrated within several regional models used in WP10. These models are: BSHCmod (DMI, model of the Baltic sea) OPA9 (DFO, model of the Newfoundland basin) Hycom (NERSC, in cooperation with SHOM) These implementations have already required a number of software developments : Simplification of the recursive calls to any part of the code optimisation of several Agrif procedures for the treatment of Fortran90 modules code transformations for run-time optimisation design of a preprocessing tool, which helps producing forcing fields for high resolution grids. This tool is available through a Java graphical interface. Subtask 7.1.2.2 : Development of the AGRIF package This task is planned to start at month 13. However, a preliminary work has been conducted to answer the needs and comments expressed by the users of the AGRIF package in WP10. Milestones reached M.7.1.1: First implementation and transfer of the global ORCA 1/4° including partial steps (Mo 12) Deliverables obtained D.7.1.1: First implementation and transfer of the global ORCA 1/4° including partial steps (Mo 12) Deviations from work plan (if any) None 9 Task 7.2: Biogeochemical modelling Scientific / Technical Progress made in this task Task 7.2.1. Biogeochemical model development and assessment The aim of this task is to develop a prognostic and robust ecosystem model that can successfully describe the observed temporal and regional variability in phytoplankton biomass and in associated carbon and nitrogen fluxes. When tested against available data, e.g. via data assimilation procedures, present ecosystem models generally fail to adequately fit the data. This suggests that models lack important ingredients and are not complex enough. On the other hand, all data assimilation studies performed so far could not constrain more than 10-15 parameters or degrees of freedom of the ecosystem models. In fact, even relatively simple NPZD-type models have already a few degrees of freedom that cannot be constrained by the presently available data, suggesting that these models might already be too complex. These apparently contradictory findings about the required level of ecosystem model complexity might indicate that current model structures are not adequate. Within this task we therefore try to make progress by a new approach fo following different structural elements for improving marine ecosystem models. The first structural element to be considered is size. Theoretical studies, laboratory studies, and empirical evidence from open-ocean systems all indicate that size and physiology are closely related. During the first year of the project we have concentrated on the role of size for nutrient uptake, phytoplankton growth, and exudation. In order to keep the number of difficult-to-constrain model parameters as small as possible, different size classes were not resolved explicitly, but a size-spectral approach was chosen instead. By combining prognostic equations for the total biomass and the number of cells with the assumption of a linear log-log spectrum, the slope of the spectrum and hence the entire particle size distribution can be diagnosed at every model time step from only two prognostic equations which, moreover, share most of the parameters. The new implicitly size-structured model attempts to use first-principles whenever possible. One of the processes implemented so far is nutrient uptake by phytoplankton that, corresponding to diffusion across cell membranes, depends on the surface-to-volume ratio and hence on the size of the cells (Aksnes & Egge, 1991). The same is assumed to hold for exudation (Bjornsen, 1988). A first consequence of these assumptions is that small cells are favoured in well-lit oligotrophic regimes, whereas a decrease in light levels causes an increase in optimum cell size (Figure 7.2.1.1). A prototype of an NPZD-type model with implicit size spectral representation in the phytoplankton has been developed and successfully tested against observations at various JGOFS time series and process study sites (Figure 7.2.1.2). Fig. 7.2.1.1. 10 Figure7.2.1.2: Annual cycle of simulated chlorophyll (mg m-3) at six time-series sites. Shading reflects more than 50% of the total chlorophyll in the <5 These initial studies have so far been used one-dimensional model configurations. The same sites will be used to calibrate the evolving model against observations using data assimilative methods and metrics currently developed in task 7.2.3 . A variant, newly developed NPZD+DON model (Huret et al., 2005) has been coupled into an improved version of the MERCATOR MNATL (1/3°) model. Interannual simulations over the 1996-1999 period have been analyzed. Model basin-scale structures are being compared with WOCE and AMT (Atlantic Meridional Transect) in the Western and Eastern Atlantic ocean, respectively (Figures 7.2.1.3 and 7.2.1.4 below). The NPZD+DOM model will also serve as starting point for developing a better mechanistic description of DOM dynamics. This will be particularly important for simulations of the carbon cycle because of the generally high carbon-to-nitrogen ratios of the dissolved organic matter. To refine this nitrogen-based ecosystem model, the work with a cell-quota model including detailed algal physiology (Salihoglu, 2005) to allow non-Redfield coupling of carbon and nitrogen cycles just got started upon arrival of Baris Salihoglu in January 2005. It is being tested in a 1D setting at the North Atlantic JGOFS time series sites. Task 7.2.2. Integration into OPA Subtask 7.2.2.1. Prototype of a coupled physical/biological assimilative system The aim of this sub-task is to assemble the basic elements of the assimilative platform needed to simulate the primary production in the North Atlantic basin at eddy-permitting resolution. The developments carried out in WP7 will be continuously incorporated to upgrade the platform in the perspective of the demonstration experiment in the North Atlantic (WP7.2.2.4). During the first year of MERSEA, a beta-version of the platform has been developed using a rigid-lid OPA model of the North Atlantic at eddy-permitting resolution (MNATL), a sequential assimilation scheme (the SEEK filter) and a first-generation biogeochemical model (N1P1-type). The different elements of the system are described here below. Physical model configuration The physical model is a z-coordinate, rigid lid, primitive-equation model based on the OPA code. The horizontal resolution is 1/3° in longitude and 1/3° cos( ) in latitude (i.e. 28 km at 40° N). On the vertical, the discretization includes 43 levels with a grid spacing that increases from 12 m at the surface to 200 m below 1500 meters. The computational domain covers the North Atlantic from 20° S to 70° N 11 latitude, and from 98.3° W to 20.6° E longitude. The Southern boundary at 20°S, the Northern boundary at 70°N, and the Gibraltar Strait are closed but the model solution is relaxed toward climatology within buffer zones defined off Portugal, in the Norwegian Sea and along the Southern boundary to simulate the supply of Mediterranean Water and the exchange with the Arctic and South Atlantic basins. The atmospheric forcing fields of heat, freshwater and momentum are derived from reanalyses of the ECMWF 6-hour forecasts. The use of prescribed heat fluxes as surface boundary conditions, however, results in unrealistic sea-surface temperature mainly because of the lack of interactivity between the ocean model and the atmosphere. To restore some kind of control on the model SST, a « flux correction » formulation is applied, so that the actual net heat flux is expressed as the sum of two components: a prescribed flux and a correction term proportional to the difference between the climatological SST and the model surface temperature. Biological model The biogeochemical model adoped in the first version of the platform is the P3ZD biogeochemical model [Aumont et al., 1998] that was developed at LODYC in order to study the oceanic carbon cycle at global scale. This model has been derived from the HAMOCC3 (HAmburg Model of Ocean Carbon Cycle) model which was initially dedicated to the study of the carbon and silica cycle but without explicit representation of phytoplankton and zooplankton dynamics. P3ZD includes 7 tracers inherited from HAMOCC3 (phosphate, dissolved inorganic carbon, alkalinity, oxygen, calcite, silicate and particulate organic carbon), with two additional compartments for phyto- and zooplankton and one compartment for dissolved organic carbon. These 10 biogeochemical variables are treated as two sub-models. The first sub-model describes the production-export-remineralization cycle (which will be the focal point for the demonstration system that is being considered here) and can be understood as a simple NPZD model plus one compartment for semi-labile dissolved organic material (i.e. N1P1-type model). The second sub- model is simply driven by the first one, and represents the cycling of carbon and silica. The first sub-model is schematically represented in figure 7.2.2.1, showing five compartments : phosphate ( PO 4 ), dissolved (DOC) and particulate organic carbon (POC), phytoplankton (PHY) and 3 zooplankton (ZOO). Although the major limiting nutrient in the ocean is nitrate, phosphate is chosen as limiting nutient in this model to get rid of nitrification processes, exchange of inorganic nitrogen with the atmosphere and bacterial degradation. Figure 7.2.2.1. Schematic representation of the production-export-remineralization cycle in P3ZD Assimilation scheme In this first prototype, the physical model variables only are governed by the assimilation process. The assimilation method is based on a Kalman filter with reduced order error covariance matrix, known as the SEEK filter. A first comparison with the assimilation run obtained using the scheme developed by Testut et al. [2003] indicates the excessive supply of nutrients in the euphotic zone through spurious mixing and advection mechanisms. This can be partly attributed to several factors, e.g. the statistical method which is unable to maintain the model constraint of hydrostatic stability, the discontinuous nature of the sequential algorithm, or the lack of consistent corrections between the physical and biological components of the 12 state vector. To overcome this problem, three variants of the assimilation algorithm have been developed to improve the representation of the model dynamics and its subsequent impact on the biological variables: (i) a “restratification” approach based on the restoration of monotonic vertical density gradient after the analysis stage; (ii) an « incremental analysis updating » scheme to ensure temporal continuity of the assimilation run, and (iii) a method combining statistical corrections of T/S properties in the mixed layer and a lifting-lowering of isopycnals below. First assessment of the coupled assimilative platform In order to roughly check how the assimilation can impact the representation of the biological fields, comparisons have been made between free runs and simulations with assimilation. In these experiments, the assimilated observations consist of Sea Surface Temperature (SST), Sea Surface Salinity and Sea Surface Height (SSH) data. The SST data originate from AVHRR observations gathered and processed within the NASA Pathfinder project (figure 7.2.2.2). The altimetric data is obtained as a sum of a time- invariant dynamic topography computed by inverse methods and Sea Level Anomalies from Aviso project combining Topex/Poseidon and ERS altimeter data. The SSS data are monthy climatological estimates from Levitus. The observation error have been specified as a sum of a measurement error (5 cm for SSH and 0.5°C for SST) and an estimated representativeness error. AVHRR SST (7 day ) TP along track data (7 day) Levitus SSS cov erag e) Figure 7.2.2.2. Model domain and examples of data sets assimilated in the physical model Simulations performed with the original assimilation scheme show unrealistic input of nutrient in the euphotic zone because of spurious assimilation-induced mixing and advection. This can be explained by the fact that purely Gaussian estimation error of T/S properties are inconsistent with the hydrostatic stability assumption made in the physical model. Annual simulations during 1998 with online coupling have been performed to assess the benefit of the new strategies by comparison with the free run, using a number of physical and biogeochemical diagnostics. Figure 7.2.2.3 illustrates the response of the system (expressed in surface chlorophyll concentration) with and without assimilation of data in the physical component of the coupled model. Coupling without assimilation Coupling with assimilation SeaWiFS data 13 Figure 7.2.2.3. Surface Chl_a concentration (in mg/m3) obtained on April 24th, 1998 in the coupled simulation without assimilation (left), in the coupled simulation with assimilation (center), and from SeaWiFS data (right) Future developments During the second year of the project, the physical model component will be upgraded using a new OPA free-surface model of the North Atlantic at 1/4° resolution and bulk parameterizations of air-sea fluxes (NATL4). The biogeochemical component will be made more complex using the second- and third generation models developed in WP7.2.2.3 (N2P1-type available at month 18, and N4P2-type available at month 36). The assimilation scheme will be upgraded using the free-surface version of the SEEK filter (available from WP7.3.2 at month 18) and improved mixed layer control (available from WP7.3.2 at month 30). Subtask 7.2.2.2. Assimilation impact on biogeochemical variables. This task has just started. Subtask 7.2.2.3. Improved biogeochemical processes To study the role of the different type of nutrients on patterns of marine primary productivity, we have used 3 differents biogeochemical model with an increased complexity approach: - A simple phosphate based model (P3ZD); - A nitrogen model (LOBSTER) including an explicit regeneration loop using ammonia; - A multi-plankton type model (PISCES) using both macronutrients (P, Si) and micronutrients (Fe). To compare these models, we have carry various experiments using same physical analysis made by the MERCATOR system (MNATL-PSY1V1). Subtask 7.2.2.4. North Atlantic bloom experiment Different biogeochemical models have been coupled in an offline mode with MERCATOR system. We have carry various experiments using same physical analysis made by the MERCATOR system (MNATL-PSY1V1) for the year 1998. New simulations using PSY1V2 system, allowing both assimilation of in-situ and satellite physical variables, are under way with a special attention on plankton burst due to shock at the base of the mixed layer induced by the assimilation procedure. Task 7.2.3. Assessment and metrics Data required for validating (satellite ocean colour and biogeochemical time-series data) and forcing for the ecosystem models have been collected. As a next step, data assimilation experiments are planned to be carried out with the biogeochemical models and developed parameter estimation schemes. Subtask 7.2.3.1. Identification of key observational data and the definition of an internal metrics. One-dimensional time-series data have been collected for the JGOFS sites BATS (320N, 640W), NABE (480N, 210W) and EUMELI as well as for the Labrador Sea and Ocean Weather Station (OWS) “India” (590N, 190W). The data sets contain: chlorophyll and nutrient data; estimates of primary and, at some stations, bacterial production; particular organic matter data are also available. In addition, SeaWiFS ocean colour data (1997-2004) and nitrate data (World Ocean Database, 1998) on a global and basin (North Atlantic) scale have been made available. At the present stage of the study, BATS chlorophyll “a”, Chlobs, and dissolved inorganic nitrogen, DINobs, data are the only data used to constrain the ecosystem models developed in WP 7.2.1. 14 C, P = Arg min { ( Chlmod - Chlobs)2 chl_obs -1 + ( DINmod - DINobs)2 DIN_obs -1 }, here, C and P ecosystem model components { Chlmod , DINmod …} and biological parameters, respectively; chl_obs and DIN_obs are the error level of the observations. Investigation of the impact of different formulations of and weightings in the cost function is needed and is presently under way. Subtask 7.2.3.2. Intercomparison and assessment of biogeochemical models: A version of the SIR filter has been applied for assimilating the Bermuda Atlantic Time-Series chlorophyll and dissolved inorganic nitrogen data into the REcoM and the current version of the size dependent NPZD ecosystem model (developed at Kiel, WP 7.2.1) in order to estimate poorly known biological parameters. Tables 7.2.3.1 and 7.2.3.2 possess results of the first parameter estimation experiments at the BATS site with the RecoM and size dependent NPZD ecosystem model, respectively. Symbol Parameter Initial Optimal Units Value Value lossN phytoplankton loss of organic nitrogen 0.05 0.048 day-1 lossC Phytoplankton loss of organic carbon 0.40 0.27 day-1 Initial slope of the P-I curve 0.10 0.12 D2W1day-1 Vp* Phytoplankton maximum drowth rate constant 0.70 0.74 day-1 resH Respiration by heterotrophs 0.01 0.008 day-1 lossH Heterotrophs mortality 0.10 0.079 day-1 Agg1 Stickiness for PCHO-PCHO 0.0075 0.0062 day-1 Agg2 Stickeness for TEP-PCHO 0.24 0.22 day-1 degChl Chlorophyll degradation rate constant 0.05 0.04 day-1 Table 7.2.3.1. RecoM biological parameters for BATS site Symbol Parameter Initial value Optimal value units P Maximum phytoplankton growth rate 2.00 1.8 day-1 Kno3 Half-saturation constant for nutrient uptake 0.05 0.026 mmolNm-3 P Phytoplankton mortality 0.03 0.016 day-1 1 Maximum phytoplankton exudation rate 0.25 0.12 day-1 Z maximum zooplankton grazing rate 2.00 1.22 day-1 kG Zooplankton ingestion half-saturation constant 0.50 0.50 mmolNm-3 kZ quadratic zooplankton mortality 0.20 0.12 mmolNm-3 2 zooplankton excretion rate 0.03 0.017 day-1 4 detritus remineralization rate 0.05 0.032 day-1 5 DON remineralization rate 0.05 0.023 day-1 Table 7.2.3.2. Size dependent NPZD model biological parameters. Figure 7.2.3.1. depicts the monthly means of the REcoM model chlorophyll (right panels) and dissolved inorganic nitrogen (left panels), simulated with the initial set of the model biological parameters (upper penels) and obtained after the data assimilation (middle panels), against the BATS chlorophyll and DIN data. 15 Figure 7.2.3.1. Results of the data assimilation experiment carried out for the size dependent NPZD ecosystem model developed in WP 7.2.1 are presented on figure 7.2.3.2. 16 Figure 7.2.3.2. As a next step, this will be expanded to the other 1D sites, before the SIR filter will also be applied to the LOBSTER and PISCES models used in WP 7.2.2 to provide best biological parameter estimates for the ecosystem model versions. Milestones reached none Deliverables obtained none Deviations from work plan The experience post-doc (Baris Salihoglu) could be recruited for the Toulouse part of subtask 7.2.1 only on January 1st, 2005, that is 8 months after the start of the project. Still, the development of the new ecosystem model is well under way,but some shifts in delivering the coupled carbon-nitrogen model may be expected. 17 Task 7.3: Data assimilation Scientific / Technical Progress made in this task Task 7.3.1: Development of advanced sequential filters Subtask 7.3.1.1. Ensemble Kalman filter -Starting point and objectives: The EnKF is an open-source freeware downloadable from http://www.nersc.no/~geir/EnKF. The assimilation of ice parameters has been studied in Lisæter et al. (2003). It is now implemented in the TOPAZ system for assimilation of ice concentrations. The forecast bulletins can be consulted on http://topaz.nersc.no. Our objective is to explore advanced assimilation schemes for ocean forecasting problems. -Progress in the task: Improved sampling procedures (cf. Evensen, Ocean Dynamics 2004) have been applied in the TOPAZ system in order to improve results for a lower CPU cost. Initital tests of different analysis schemes have been performed (e.g. perturbations of measurements and square-root schemes) All improved routines are served and documented on the EnKF web page. A bias reduction technique has been tested in the context of assimilation of simulated « CRYOSAT-like » ice thickness into a half-resolution prototype of the TOPAZ system. The tests show measurable improvements and an article is under preparation (Lisæter 2005). Subtask 7.3.1.2. SIR filter The required software for coupling SIRF to ecosystem models has been developed. The guided resampling has now been implemented and put into the SIRF code in order to reduce the number of model trajectory ensemble members needed for the data assimilation scheme.Validation of the data assimilation technique with the RecoM and size dependent NPZD ecosystem models is planned for the next few months. A version of the SIR is being tested which uses local updating.This means that only those ensemble members are usedin the ensemble that are close to the observations locally, and members from different locations are glued together using the relative weights of the ensemble members, since the latter will be smooth functions of space. Subtask 7.3.1.3. Adaptive SEEK filter Task not started. Task 7.3.2: Global OPA data assimilation system Subtask 7.3.2.1 Transition to free surface model During the first year of the project, the coupling of altimetric data assimilation with OPA free surface global configuration has been implemented, and an algorithm (IAU scheme) to properly initialise the model forecast (eliminating inconsistent initial conditions) has been developed. This has been evaluated using twin experiments with the ORCA2 global configuration, with an example of the control of model errors. a. The model configuration 18 The OPA global free surface configuration that will be used for the development of the assimilation scheme is the ORCA2 configuration. This is a low resolution configuration (about 2°x2°, with a meridional grid spacing refinement going down to 1/2° in the tropical regions) from the ORCA grid family (see Figure 7.3.2.1). This is coherent with OPA model developments (task 7.1) and operational applications (WP9) which are using global configurations from the same ORCA grid family. The low resolution is justified here (task 7.3.2) in a first stage because the first development of the assimilation that are scheduled are not very affected by the model resolution. It is then only in a second stage that the new assimilation developments will be checked with higher resolution regional model. A good candidate for this (again from the ORCA grid family) is the North Atlantic 1/4° resolution configuration (NATL4). The model air-sea fluxes are computed from bulk formulation, feeded by daily NCEP data and ERS/TAO winds. Relaxation to Levitus surface salinity climatology is applied. b. The assimilation scheme The next generation of assimilation schemes that is currently being transferred to the MERSEA operational system is derived from a reduced order Kalman filter (the SEEK filter) developed at LEGI for the last few years. Figure 7.3.2.2 gives a summary of the SEEK filter possibilities. The objective of this task (7.3.2) is to provide the developments of the scheme that are necessary to fit the requirements of operational use, focusing Figure 7.3.2.1: ORCA2 model grid. on those that are most expected to generate immediate, first order and low cost operational benefit. Moreover, the developments that are planned in this project are quite independent of the particular scheme in use, and could potentially be plugged in most sequential assimilation algorithm. The SEEK filter was chosen because it is thought to make a significant progress with respect to currently used optimal interpolation scheme, and because of its immediate availability. It is obvious, however, that it is affected by well-known difficulties that need to be considered to build the next generation of assimilation schemes. This is the purpose of task 7.3.1 (see above). As any other Kalman filter, the SEEK filter requires a parameterisation of the error covariance on the various sources of information: initial condition (P°), observations (R), and model (Q), that need to be thought specifically for any kind of experiment (see below). Concerning the assimilation of altimetry into ORCA2, we always use in this study an estimation space covering all variables of the state vector: temperature (T), salinity (S), zonal (U) and meridional (V) velocity. c. The initialisation scheme 19 In the standard Kalman filter, it is assumed that a correction is computed and applied to the model state each time a new observation is available. In our implementation, however, as done in most applications, and practically in all ocean basin-scale operational system currently running, we divide the time in a number of equal periods or assimilation windows. For each assimilation window, we gather all innovations collected during that assimilation window, and we make only one analysis and model correction at the end of each assimilation window. The justification for such suboptimal scheme is that, having more observations for each statistical analysis, the solution is less sensitive to approximation in the parameterisation of the various error covariance matrices (P°, R, Q). The optimal assimilation window is then a compromise between the minimisation of the model drift between two successive analyses, and the minimisation of spurious effects due to inadequate error parameterisation. This problem is of course amplified by the lack of observations, which is a constant preoccupation in ocean assimilation systems, most often underconstrained by the observations. In our study, the assimilation window has been set to 5 days. The consequence of this situation is that the correction that we apply to the model can often be Figure7.3.2.2: Schematic of the SEEK non negligible with respect to the assimilation method. signal that we are controlling. This leads to two kinds of problems: (i) significant time discontinuity of the solution, and (ii) spurious high frequency oscillations (like gravity waves in a free surface ocean model), if no adequate initialisation procedure is applied. The purpose of this subtask (7.3.2.1) is precisely to eliminate these two problems from global OPA free surface configuration with altimetric data assimilation. In order to tackle these problems, Bloom et al. (1996) have proposed an algorithm called Incremental Analysis Update (IAU), consisting in incorporating the analysis increment in a gradual manner. There are many variants of the IAU algorithm, differing by the position of the time window during which the increment is incorporated. The variant that we have chosen for this study can be described as follows (see figure 7.3.2.3): a first guess model forecast is done from time i to i+1 and an analysis is performed at time i+1. An increment is calculated on the state vector using the Kalman gain as in the usual Kalman filter. Then the model is run again from time i to i+1, but with the increments applied gradually to all variables of the state vector. The obtained state at i+1 will then provide the initial conditions for the next model forecast. It is expected that these new initial condition remain close to the Kalman analysis state, but that any unbalanced or non-physical increment has been removed. In that sense, this IAU forecast can also be viewed as a model reinitialisation procedure. During this IAU forecast, the additional forcing term imposed to the model equations is a fraction of the analysis increment x: dx/dt = M (t) x + (t) x with 0tc (t) dt In the present case, as in most IAU methodologies, this forcing term is the increment divided by the duration of the assimilation cycle tc. 20 (t) = 1/tc Hence, this value is constant during the IAU forecast and the time-integrated quantity of this value over the IAU forcing time window is the increment. This variant of the IAU scheme leads to an increase in the integration time of the model of 100%. This particular variant was chosen because it offers the possibility to compare the forecast, the analyzed and the IAU corrected states at the same time. In the IAU variant from Bloom et al. (1996), half of the increment is applied before the analysis time and half of the increment is applied after the analysis time, only increasing the cpu time by 50%. d. Twin assimilation experiments Figure 7.3.2.3: Schematic of the IAU initialisation scheme. This assimilation system (the SEEK filter assimilating altimetric data within ORCA2) with the new IAU initialisation scheme has been tested using twin experiments. (Realistic assimilation experiments with higher resolution model [NATL4] will be performed before this system is delivered [at month18].) The experiment setup is designed in such a way that the only source of error in the system is model error. This is indeed an efficient way to produce a strong drift of the model during one assimilation cycle, leading to large SEEK corrections, and hence, an effective checking of the IAU protocol. The reference simulation (the true ocean) is the standard ORCA2 interannual simulation for the year 1993. Synthetic altimetric observations are then sampled from this reference simulation to be assimilated in a modified simulation (the false ocean) in which the atmospheric forcing has been altered. The initial condition has been kept the same, so that the only source of error is the atmospheric forcing: the wind stress has been doubled in the tropical zone (between -30° and +30°) with respect to the reference simulation. Again such unrealistically large model error has been produced in order to provide an adequate platform for IAU evaluation. Since it is not the purpose here to study the way to produce an effective error subspace for the SEEK filter, we choosed an ideal situation on that respect (which does not affect the validity of the IAU evaluation). We assume that the true model is known so that we can generate an ensemble of differences between the true and false model 5-day forecasts from an ensemble of initial conditions (the same for the 2 models). The covariance of this ensemble is then a good estimator of the 5-day model error covariance, and is adequate to parameterize the forecast error covariance in the SEEK filter (assuming that the initial error covariance remains small cycle after cycle). As an ensemble of initial conditions, we used model states from the reference simulation every 5 days (73 members). From each initial condition, we run the true and false model 5-day forecasts using forcing data synchronized with the initial conditions, this allowing to sample also the forcing variability over the year 1993. From the ensemble of differences after 5 days, we retain only the first 30 EOFs to parameterize the SEEK forecast error covariance matrix. Moreover, we used this matrix globally as it is, without any further local parameterization. e. Evaluation of the scheme Figure 7.3.2.4 and 7.3.2.5 show the RMS error for the false ocean without assimilation (green curve) and 21 with assimilation of altimetric data (red curve). The figure also shows the RMS error for the first guess forecasts (without the IAU increment) using the false model (black curves) and for the SEEK analysis applied to these forecasts (black circles). This is the analysis error before IAU initialisation. We can see from the figure that the IAU initialisation only slightly increase the analysis error, while we expect a substantial benefit in the forecast. Figures 7.3.2.6, 7.3.2.7 and 7.3.2.8 present the Figure 7.3.2.4.: Temperature RMS error. annual mean solution obtained in the Tropical Pacific: mean sea surface elevation (figure 7.3.2.5), mean zonal velocity (figure 7.3.2.6) and mean temperature section (figure 7) along the equator. The true ocean, the false ocean and the assimilation solution (IAU analysis) are compared, demonstrating the ability of the assimilation scheme to correct the wind model error successfully and drive the false model close to the true ocean. A closer look to the behaviour of the scheme, assimilation cycle after assimilation cycle, whows that this is due to the ability of the IAU increment (on temperature, salinity, and velocity) to compensate precisely the model error due to the wind. In the deep ocean, it is the temperature and salinity increment that is most important, while close to the surface, the velocity increment is required. This study demonstrates an implementation of the IAU initialisation scheme into an OPA free surface global data assimilation system. Before delivery of this system (at month 18) it will be evaluated using a Figure 7.3.2.5: Sea surface elevation RMS error 1-year hindcast experiment (i.e. using real data) with a higher resolution regional model (NATL4). During this second phase of IAU evaluation, we will focus on the diagnostic of the model forecast from IAU initial conditions. This is indeed where we expect the best improvement with respect to previous results. 22 Figure 7.3.2. 6: Annual mean sea surface elevation for the true ocean (top left), the false ocean (top right) and the assimilation solution (bottom). Figure 7.3.7.: Annual mean zonal velocity along the equator for the true ocean (top left), the false ocean (top right) and the assimilation solution (bottom). 23 Figure 7.3.2.8.: Annual mean temperature along the equator for the true ocean (top left), the false ocean (top right) and the assimilation solution (bottom). 7.3.2.2 In situ/satellite data assimilation scheme During the first year of the project, a data assimilation scheme assimilating jointly in situ and satellite observations into OPA free surface global configuration has been implemented (including the IAU scheme to initialize the model forecast). a. The assimilation system The assimilation system is here the same as in subtask 7.3.2.1. Again, the ORCA2 model is used for assimilation development purpose. The assimilation scheme is also the SEEK filter, together with the IAU initialisation scheme. The first difference lays in the observation space which now includes temperature and salinity profiles in addition to altimetry. The purpose is indeed to develop and evaluate this system (analysis scheme and initialisation protocol) to assimilate jointly in situ and satellite observations. For that purpose, we will go a little further in the realism of the simulations that we are going to perform: we will still evaluate the system using twin experiments, but we will not assume anymore that we know the true model when we estimate the SEEK forecast error covariance matrix. 24 In that context, the SEEK error parameterization will be less reliable, and the local SEEK approximation will be required. Indeed, when a reduced order representation of a covariance matrix is used, the confidence interval for small correlation coefficients is always the largest. As a consequence, long range correlation coefficients (which are usually small) are often overestimated, inducing spurious long-range observation influence. The local approximation of the SEEK filter has been designed to eliminate these lon-range influence by setting the corresponding correlation coefficients to zero. In practice, this means that the model state vector is divided in a number of subsystems for which separate statistical analyses are performed. Each analysis is applied on the set of observations that is inside the influence bubble of the corresponding subsystem. These influence bubbles must be parameterized; they define the spatial domain outside which the correlation coeffcients are set to zero. In the implementation of the local SEEK filter applied in MERSEA, the subsystems of the model state are specified as the water columns of the 3D ocean model. The influence bubbles of every subsystems are then specified as a floating mask (of adjustable dimension) centered successively on every water columns of the ocean model. As a consequence, an upgrade of the algorithm was needed to apply this scheme to a global model with grid periodicity conditions. For instance, a subsystem close to the Western edge of the grid must be associated to an influence bubble extending also close to the Eastern edge of the grid, which corresponds to the same geographical region. Hence, when the floating mask defining the influence bubble is pasted onto the Figure 7.3.2.9.: Correction obtained from a single model grid around such subsystem, care must be observation close to the Eastern edge of the grid. taken to cut the part of the mask extending further than the Western (resp. Eastern) limit of the grid and paste it at the Eastern (resp. Western) limit of the grid. Figure 7.3.2.8 shows an example of the increment generated by the analysis of one observation close to the Eastern edge of the grid with this enhancement of the local SEEK algorithm. This procedure is obviously necessary to ensure the continuity of the analysis accross the grid cut. b. Twin assimilation experiments This assimilation system (the SEEK filter assimilating altimetric and in situ data within ORCA2) with the IAU initialisation scheme is being tested using twin experiments. Again, as in subtask 7.3.2.1, the experiment setup is designed in such a way that the only source of error in the system is model error. But, here, instead of doubling the wind in the tropical regions, we decided to introduce perturbations in key parameters of the bulk formulation for the air-sea fluxes of heat and fresh water. This kind of simulation will also be used in subtask 7.3.2.3 (from months 19 to 30) to evaluate the possibility to correct the model atmospheric forcing by controlling the bulk formulation parameters. Here, we use the same kind of experimental setup with an assimilation scheme that corrects the model state vector only. The reference simulation (the true ocean) is the standard ORCA2 interannual simulation for the year 1993, with the original bulk formula. Synthetic altimetric, temperature and salinity (profiles) observations are then sampled from this reference simulation to be assimilated in a modified simulation (the false ocean) in which the bulk formulation has been modified. Four possible perturbations have been H W (Tw-Ta specific heat, W is the wind speed, Tw and Ta are the sea and air temperatures) can be simplified using constant bulk coefficient CH instead of the default complex parameterization, (ii) the latent heat flux QL E W max(0,qs-qa speed, qs and qa are the surface and atmospheric specific humidities) can be simplified using constant bulk 25 coefficient CE instead of the default complex parameterization, (iii) the precipitation can be multiplied by a constant factor CP, and (iv) the cloudiness can be multiplied by a constant factor CC. In this experiment, the false ocean is defined by the set of parameters: CH=10-3, CE=1.12 10-3, CP=1, CC=1. In this experiment, we do not assume anymore (as in the twin experiments presented in subtask 7.3.2.1) that the true model is known to parameterize the forecast error covariance matrix. In order to generate a sensible parameterization, we still apply the same kind of procedure as in subtask 7.3.2.1, only replacing the true model where it is used by an ensemble of models with different values for the bulk parameters. Table 1 summarizes the ensemble of bulk parameters that have been used. The dispersion of the parameters in this ensemble should be consistent with a priori information on the parameters error covariance. We also keep an ensemble of initial conditions (the same as in subtask 7.3.2.1) to increase the size of the sample of 5-day forecast differences. From this sample, we retain only the first 20 EOFs to parameterize the SEEK forecast error covariance matrix. Member CE CH CC CP 1 1.12 10-3 1. 10-3 1 1 2 1.00 10-3 1. 10-3 1 1 3 0.90 10-3 1. 10-3 1 1 Table 1. 4 1.20 10-3 1. 10-3 1 1 Ensemble of 5 1.30 10-3 1. 10-3 1 1 bulk parameters 6 1.12 10-3 0.90 10-3 1 1 7 1.12 10-3 0.80 10-3 1 1 8 0.50 10-3 0.50 10-3 1 1 9 0.10 10-3 0.10 10-3 1 1 10 0.60 10-3 0.60 10-3 1 1 c. Evaluation of the scheme First test experiments are currently being performed. The evaluation of the scheme on the twin experiment presented above must be continued in the next 6 months, so as to obtain a scheme usable by the subtask 7.3.2.3 between month 19 and month 30. Then, the developments in substask 7.3.2.2 will be suspended until month 30. At that time, the work in task 7.3.2.2 will resume: combining the results obtain in subtask 7.3.2.1 and 7.3.2.3, we will (i) perform a reanalysis with ORCA2, (ii) evaluate the possibility for the assimilation in a higher resolution model (i.e. NATL4) to benefit from previous assimilation in a lower resolution model (i.e. ORCA2), and (iii) perform a reanalysis using a regional higher resolution model (i.e. NATL4) using all developments obtained in task 7.3.2. Milestones reached None Deliverables obtained None 26 Deviations from work plan (if any) None. Table 1: Deliverables List Del Deliverabl Workpa Date Actual/F Estimated Used Lead contractor . e name ckage due orecast indicative indicati no. no. delivery person- ve date months *) person- months *) 7.1. First WP7.1 31/05/0 31/05/200 32.5 32.5 CNRS/ 1 version of 5 5 LEGI the global 1/4° model ORCA- R025 *) if available Table 2: Milestones List Milestones are intermediate steps leading to Deliverables, but distinct from them Milestone Milestone name Workpackage Date due Actual/Forecast Lead no. no. delivery date contractor 7.1.1 First version of the WP7.1 31/05/2005 31/05/2005 CNRS/ global 1/4° model LEGI ORCA-R025 27