The Mercator global ocean operational analysis system: Assessment and validation of an 11-year reanalysis

This paper presents Prototype Système 2 Global (PSY2G), the first Mercator global Ocean General Circulation Model (OGCM) to assimilate along-track sea level anomaly (SLA) satellite data. Based on a coarse resolution ocean model, this system was developed mainly for climatic purposes and will provide, for example, initial oceanic states for coupled ocean-atmosphere seasonal predictions. It has been operational since 3 September 2003 and produces an analysis and a two-week forecast for the global ocean every week. The PSY2G system uses an incremental assimilation scheme based on the Cooper and Haines [Cooper, M., Haines, K., 1996. Data assimilation with water property conservation. J. Geophys. Res., 101, 1059-1077.] lifting–lowering of isopycnals. The SLA increment is obtained using an optimal interpolation method then the correction is partitioned into baroclinic and barotropic contributions. The baroclinic ocean state correction consists of temperature, salinity and geostrophic velocity increments and the barotropic correction is a barotropic velocity increment. A reanalysis (1993–2003) was carried out that enabled the PSY2G system to perform its first operational cycle. All available SLA data sets (TOPEX/Poséïdon, ERS2, Geosat-Follow-On, Jason1 and Envisat) were assimilated for the 1993–2003 period. The major objective of this study is to assess the reanalysis from both an assimilation and a thermodynamic point of view in order to evaluate its realism, especially in the tropical band which is a key region for climatic studies. Although the system is also able to deliver forecasts, we have mainly focused on analysis. These results are useful because they give an a priori estimation of the qualities and capabilities of the operational ocean analysis system that has been implemented. In particular, the reanalysis identifies some regional biases in sea level variability such as near the Antarctic Circumpolar Current, in the eastern Equatorial Pacific and in the Norwegian Sea (generally less than 1 cm) with a small seasonal cycle. This is attributed to changes in mean circulation and vertical stratification caused by the assimilation methodology. But the model's low resolution, inaccurate physical parameterisations (especially for ocean–ice interactions) and surface atmospheric forcing also contribute to the occurrence of the SLA biases. A detailed analysis of the thermohaline structure of the ocean reveals that the isopycnal lifting–lowering tends to diffuse vertically the main thermocline. The impact on temperature is that the surface layer (0–200 m) becomes cooler whereas in deeper waters (from 500 to 1500 m), the ocean becomes slightly warmer. This is particularly true in the tropics, between 30°N and 30°S. However it can be demonstrated that the assimilation improves the variability in both surface currents and sub-surface temperature in the Equatorial Pacific Ocean.     

Validation of a hybrid optimal interpolation and Kalman filter scheme for sea surface temperature assimilation

A hybrid data assimilation scheme designed for operational assimilation of satellite sea surface temperatures (SST) into an ocean model has been developed and validated against in-situ observations. The scheme consists of an optimal interpolation (OI) part and a greatly simplified Kalman filter (KF) part.The OI is performed only in the longitudinal and latitudinal directions. A climatological field is used as a background field for the interpolation. It is constructed by fitting daily averages of satellite SST to the annual mean, annual, and semiannual harmonics in a 20 km by 20 km grid. The background error covariance is approximated by a spatially varying two-dimensional exponential covariance model. The parameters of the covariance model are fitted to the deviations of the satellite data from the background field using data from a full year.The simplified KF uses ocean model forecasts as a background field. It is based on the assumption that it is possible to neglect horizontal SST covariances in the filter and that the typical time scale for vertical mixing in the mixed layer is much shorter than the average time between observations. We therefore assume that the error variance in a column of water is evenly spread out throughout the mixed layer. The result of these simplifications is a computationally very efficient KF.A one year validation of the scheme is performed for year 2001 using an operational eddy resolving ocean model covering the North Sea and the Baltic Sea. It is found that assimilation of sea surface temperature data reduces the model root mean square error from 1.13 °C to 0.70 °C. The hybrid scheme is found to reduce the root mean square error slightly more than the simplified KF without OI to 0.66 °C. The inclusion of spatially varying satellite error variances does not improve the performance of the scheme significantly.     

1/32° real-time global ocean prediction and value-added over 1/16° resolution

A 1/32° global ocean nowcast/forecast system has been developed by the Naval Research Laboratory at the Stennis Space Center. It started running at the Naval Oceanographic Office in near real-time on 1 Nov. 2003 and has been running daily in real-time since 1 Mar. 2005. It became an operational system on 6 March 2006, replacing the existing 1/16° system which ceased operation on 12 March 2006. Both systems use the NRL Layered Ocean Model (NLOM) with assimilation of sea surface height from satellite altimeters and sea surface temperature from multi-channel satellite infrared radiometers. Real-time and archived results are available online at http://www.ocean.nrlssc.navy.mil/global_nlom. The 1/32° system has improvements over the earlier system that can be grouped into two categories: (1) better resolution and representation of dynamical processes and (2) design modifications. The design modifications are the result of accrued knowledge since the development of the earlier 1/16° system. The improved horizontal resolution of the 1/32° system has significant dynamical benefits which increase the ability of the model to accurately nowcast and skillfully forecast. At the finer resolution, current pathways and their transports become more accurate, the sea surface height (SSH) variability increases and becomes more realistic and even the global ocean circulation experiences some changes (including inter-basin exchange). These improvements make the 1/32° system a better dynamical interpolator of assimilated satellite altimeter track data, using a one-day model forecast as the first guess. The result is quantitatively more accurate nowcasts, as is illustrated by several model-data comparisons. Based on comparisons with ocean color imagery in the northwestern Arabian Sea and the Gulf of Oman, the 1/32° system has even demonstrated the ability to map small eddies, 25–75 km in diameter, with 70% reliability and a median eddy center location error of 22.5 km, a surprising and unanticipated result from assimilation of altimeter track data. For all of the eddies (50% small eddies), the reliability was 80% and the median eddy center location error was 29 km. The 1/32° system also exhibits improved forecast skill in relation to the 1/16° system. This is due to (a) a more accurate initial condition for the forecast and (b) better resolution and representation of critical dynamical processes (such as upper ocean – topographic coupling via mesoscale flow instabilities) which allow the model to more accurately evolve these features in time while running in forecast mode (forecast atmospheric forcing for the first 5 days, then gradually reverting toward climatology for the remainder of the 30-day forecast period). At 1/32° resolution, forecast SSH generally compares better with unassimilated observations and the anomaly correlation of the forecast SSH exceeds that from persistence by a larger amount than found in the 1/16° system.     

Altimeter assimilation in the OCCAM global model: Part I: A twin experiment

An altimeter data assimilation scheme has been tested in the OCCAM (Ocean Circulation and Climate Advanced Modelling) global 1/4°, 36-level model using a twin experiment format. The Cooper and Haines displacement scheme is used. The method works well in most regions and depths. Currents and densities in the top 1000 m generally improve by over 50–70% after 5 months of sea level assimilation every 15 days. Below 1000 m, an error reduction of up to 50% is achieved. The errors remain low during a further 60-day run without assimilation. Diagnostics for the North Atlantic, the Tropical Pacific and the Antarctic Circumpolar Current are shown alongside the global averages.The main problems encountered were in weakly stratified regions of the Antarctic and Arctic seas. A scale selective filter is developed to avoid assimilating scales much larger than the local deformation radius, and this avoids the adverse assimilation effects in the southern oceans. A companion paper uses this scheme to assimilate TOPEX and ERS-1 altimeter maps.     

A Mean Dynamic Topography of the Mediterranean Sea computed from altimetric data, in-situ measurements and a general circulation model

In the Mediterranean Sea, where the mean circulation is largely unknown and characterized by smaller scales and less intensity than in the open ocean, the interpretation of altimetric Sea Level Anomalies (SLA) is rather difficult. In the context of operational systems such as MFS (Mediterranean Forecasting System) or MERCATOR, that assimilate the altimetric information, the estimation of a realistic Mean Dynamic Topography (MDT) consistent with altimetric SLA to be used to reconstruct absolute sea level is a crucial issue. A method is developed here to estimate the required MDT combining oceanic observations as altimetric and in-situ measurements and outputs from an ocean general circulation model (OGCM).In a first step, the average over the 1993–1999 period of dynamic topography outputs from MFS OGCM provides a first guess for the computation of the MDT. Then, in a second step, drifting buoy velocities and altimetric data are combined using a synthetic method to obtain local estimates of the mean geostrophic circulation which are then used to improve the first guess through an inverse technique and map the MDT field (hereafter the Synthetic Mean Dynamic Topography or SMDT) on a 1/8° resolution grid.Many interesting current patterns and cyclonic/anticyclonic structures are visible on the SMDT obtained. The main Mediterranean coastal currents are well marked (as the Algerian Current or the Liguro–Provenço–Catalan Current). East of the Sicily channel, the Atlantic Ionian Stream divides into several main branches crossing the Ionian Sea at various latitudes before joining at 19°E into a unique Mid-Mediterranean Jet. Also, strong signatures of the main Mediterranean eddies are obtained (as for instance the Alboran gyre, the Pelops, Ierapetra, Mersa-Matruh or Shikmona anticyclones and the Cretan, Rhodes or West Cyprius cyclones). Independent in-situ measurements from Sea Campaigns NORBAL in the North Balearic Sea and the North Tyrrhenian Sea and SYMPLEX in the Sicily channel are used to validate locally the SMDT: deduced absolute altimetric dynamic topography compares well with in-situ observations. Finally, the SMDT is used to compute absolute altimetric maps in the Alboran Sea and the Algerian Current. The use of absolute altimetric signal allows to accurately follow the formation and propagation of cyclonic and anticyclonic eddies in both areas.     

Assimilation of altimetric data in the mid-latitude oceans using the Singular Evolutive Extended Kalman filter with an eddy-resolving, primitive equation model

A new data assimilation scheme has been elaborated for ocean circulation models based on the concept of an evolutive, reduced-order Kalman filter. The dimension of the assimilation problem is reduced by expressing the initial error covariance matrix as a truncated series of orthogonal perturbations. This error sub-space evolves during the assimilation so as to capture the growing modes of the estimation error. The algorithm has been formulated in quite a general fashion to make it tractable with a large variety of ocean models and measurement types. In the present paper, we have examined three possible strategies to compute the evolution of the error subspace in the so-called Singular Evolutive Extended Kalman (SEEK) filter: the steady filter considers a time-independent error sub-space, the apprentice filter progressively enriches the error sub-space with the information learned from the innovation vector after each analysis step, and the dynamical filter updates the error sub-space according to the model dynamics. The SEEK filter has been implemented to assimilate synthetic observations of the surface topography in a non-linear, primitive equation model that uses density as vertical coordinate. A simplified box configuration has been adopted to simulate a Gulf Stream-like current and its associated eddies and gyres with a resolution of 20 km in the horizontal, and 4 levels in the vertical. The concept of twin experiments is used to demonstrate that the conventional SEEK filter must be complemented by a learning mechanism in order to model the misrepresented tail of the error covariance matrix. An approach based on the vertical physics of the isopycnal model, is shown particularly robust to control the velocity field in deep layers with surface observations only. The cost of the method makes it a suitable candidate for large-size assimilation problems and operational applications.     

Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter

Ocean-biogeochemical models show typically significant errors in the representation of chlorophyll concentrations. The model state can be improved by the assimilation of satellite chlorophyll data with algorithms based on the Kalman filter. However, these algorithms do usually not account for the possibility that the model prediction contains systematic errors in the form of model bias. Accounting explicitly for model biases can improve the assimilation performance. To study the effect of bias estimation on the estimation of surface chlorophyll concentrations, chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) are assimilated on a daily basis into the NASA Ocean Biogeochemical Model (NOBM). The assimilation is performed by the ensemble-based SEIK filter combined with an online bias correction scheme. The SEIK filter is simplified here by the use of a static error covariance matrix. The performance of the filter algorithm is assessed by comparison with independent in situ data over the 7-year period 1998–2004. The bias correction results in significant improvements of the surface chlorophyll concentrations compared to the assimilation without bias estimation. With bias estimation, the daily surface chlorophyll estimates from the assimilation show about 3.3% lower error than SeaWiFS data. In contrast, the error in the global surface chlorophyll estimate without bias estimation is 10.9% larger than the error of SeaWiFS data.     

Data assimilation into a Princeton Ocean Model of the Mediterranean Sea using advanced Kalman filters

This study investigates the effectiveness of the Singular Evolutive Extended Kalman filter (SEEK) and its variants (SEIK and SFEK filters) for data assimilation into a Princeton Ocean Model (POM) of the Mediterranean Sea. The SEEK filters are sub-optimal Kalman filters based on the approximation of the filter's error covariance matrices by singular low-rank matrices, reducing in this way extensive computational burden. At the initialization, the filters error covariance matrix is parameterized by a set of multivariate empirical orthogonal functions (EOFs) which describe the dominant modes of the system's variability. The Mediterranean model is implemented on a 1/4° × 1/4° horizontal grid with 25 sigma levels and is forced with 6-hour ECMWF re-analysis atmospheric data. Several twin experiments, in which pseudo-observations of altimetric data and/or data profiles were assimilated, were first performed to evaluate the filters performances and to study their sensitivities to different parameters and setups. The results of these experiments were very encouraging and helped in setting up an effective configuration for the assimilation of real data in near-real time situation. In the hindcast experiments, Topex/Poseidon and ERS weekly sea level anomaly data were first assimilated during 1993 and the filters solution was evaluated against independent Reynolds sea surface temperature (SST) analysis. The assimilation system was able to significantly enhance the consistency between the model and the assimilated data, although the improvement with respect to independent SST data was significantly less pronounced. The model SST was only improved after including SST data in the assimilation system.     

Assessment of sea surface temperature observational networks in the Baltic Sea and North Sea

The satellite and in situ Sea Surface Temperature (SST) observational networks in the Baltic Sea and North Sea are evaluated based on the quality of the gridded SST products generated from the networks. A multi-indicator approach is applied in the assessment. It includes evaluation of data quality, effective data coverage, field reconstruction error and model nowcast error. The results show that the best available full-coverage SST product is generated by assimilating the SST observations to obtain a yearly mean model bias of 0.07 °C and RMSE of 0.64 °C. The effective data coverage rate is 31% by using AVHRR (Advanced Very High Resolution Radiometer) data from NOAA (National Ocean and Atmosphere Administration) satellites 12, 14 and 16. The data redundancy increases rapidly with the number of infrared sensors. Using either NOAA satellite 12 or all 3 satellites makes a small difference with regard to derived effective coverage and the ocean model nowcast error. The influence of using the in situ SST observations in the SST field reconstruction is negligibly small. Instead, the major role of in situ SST observations is in calibrating the satellite observations. To study the relative importance of data quality and data coverage, an assessment is done for two satellite products: one product is based entirely on NOAA 12 data and has larger coverage but lower quality. The other product is a subset of the SAF products (derived from NOAA 14 and 16) and has lower coverage but higher quality. Based on current monitoring, modelling and assimilation technology, the results suggest that the data quality is an important factor in further improving the quality of the gridded SST products. Recommendations are made for possible further improvements of the existing SST observational networks.     

Modelling the silica pump in the Permanently Open Ocean Zone of the Southern Ocean

A coupled 1D physical–biogeochemical model has been built to simulate the cycles of silicon and of nitrogen in the Indian sector of the Permanently Open Ocean Zone of the Southern Ocean. Based on a simplified trophic network, that includes two size classes of phytoplankton and of zooplankton, and a microbial loop, it has been calibrated by reference to surface physical, chemical and biological data sets collected at the KERFIX time-series station (50°40′S–68°25′E). The model correctly reproduces the high nutrient low chlorophyll features typical of the studied area. In a region where the spring–summer mixed layer depth is usually deeper than 60 m, the maximum of chlorophyll never exceeds 1.5 mg m⁻³, and the annual primary production is only 68 g C m⁻² year⁻¹. In the surface layer nitrate is never exhausted (range 27–23.5 mmoles m⁻³) while silicic acid shows strong seasonal variations (range 5–20 mmoles m⁻³). On an annual basis 71% of the primary production sustained by nanophytoplankton is grazed by microzooplankton. Compared to North Atlantic, siliceous microphytoplankton is mainly prevented from blooming because of an unfavourable spring–summer light-mixing regime. Silicic acid limitation (high half saturation constant for Si uptake: 8 mmoles m⁻³) also plays a major role on diatom growth. Mesozooplankton grazing pressure excerpts its influence especially in late spring. The model illustrates the efficiency of the silica pump in the Southern Ocean: up to 63% of the biogenic silica that has been synthetized in the photic layer is exported towards the deep ocean, while only 11% of the particulate organic nitrogen escapes recycling in the surface layer.     

Improving the physics of a coupled physical–biogeochemical model of the North Atlantic through data assimilation: Impact on the ecosystem

Several studies on coupled physical–biogeochemical models have shown that major deficiencies in the biogeochemical fields arise from the deficiencies in the physical flow fields. This paper examines the improvement of the physics through data assimilation, and the subsequent impact on the ecosystem response in a coupled model of the North Atlantic. Sea surface temperature and sea surface height data are assimilated with a sequential method based on the SEEK filter adapted to the coupling needs. The model domain covers the Atlantic from 20°S to 70°N at eddy-permitting resolution. The biogeochemical model is a NPZD-DOM model based on the P3ZD formulation. The results of an annual assimilated simulation are compared with an annual free simulation.With assimilation, the representation of the mixed layer depth is significantly improved in mid latitudes, even though the mixed layer depth is generally overestimated compared to the observations. The representation of the mean and variance of the currents is also significantly improved.The nutrient input in the euphotic zone is used to assess the data assimilation impact on the ecosystem. Data assimilation results in a 50% reduction of the input due to vertical mixing in mid-latitudes, and in a four- to six-fold increase of the advective fluxes in mid-latitudes and subtropics. Averaged zonally, the net impact is a threefold increase for the subtropical gyre, and a moderate (20–30%) decrease at mid and high latitudes.Surface chlorophyll concentration increases along the subtropical gyre borders, but little changes are detected at mid and high latitudes. An increase of the primary production appears along the Gulf Stream path, but it represents only 12% on average for mid and high latitudes. In the subtropical gyre centre, primary production is augmented but stays underestimated (20% of observations). These experiments show the benefits of physical data assimilation in coupled physical–biogeochemical applications.     

Decadal-scale variations of trophic levels at high trophic levels in the Yellow Sea and the Bohai Sea ecosystem

A total of 2759 stomachs collected from a bottom trawl survey carried out by R/V “Bei Dou” in the Yellow Sea between 32°00 and 36°30N in autumn 2000 and spring 2001 were examined. The trophic levels (TL) of eight dominant fish species were calculated based on stomach contents, and trophic levels of 17 dominant species in the Yellow Sea and the Bohai Sea reported in later 1950s and mid-1980s were estimated so as to be comparable. The results indicated that the mean trophic level at high trophic levels declined from 4.06 in 1959–1960 to 3.41 in 1998–1999, or 0.16–0.19·decade^− 1 (mean 0.17·decade^− 1) in the Bohai Sea, and from 3.61 in 1985–1986 to 3.40 in 2000–2001, or 0.14·decade^− 1 in the Yellow Sea; all higher than global trend. The dominant species composition in the Yellow Sea and the Bohai Sea changed, with the percentage of planktivorous species increases and piscivorous or omnivorous species decreases, and this was one of the main reasons for the decline in mean trophic level at high tropic levels. Another main reason was intraspecific changes in TL. Similarly, many factors caused decline of trophic levels in the dominant fish species in the Yellow Sea and the Bohai Sea. Firstly, TL of the same prey got lower, and anchovy (Engraulis japonicus) as prey was most representative. Secondly, TLs of diet composition getting lower resulted in not only decline of trophic levels but also changed feeding habits of some species, such as spotted velvetfish (Erisphex pottii) and Trichiurus muticus in the Yellow Sea. Thirdly, species size getting smaller also resulted in not only decline of trophic levels but also changed feeding habits of some species, such as Bambay duck (Harpodon nehereus) and largehead hairtail (Trichiurus haumela). Furthermore, fishing pressure and climate change may be interfering to cause fishing down the food web in the China coastal ocean.     

Comparison between a reanalyzed product by 3-dimensional variational assimilation technique and observations in the Ulleung Basin of the East/Japan Sea

Reanalyzed products from a MOM3-based East Sea Regional Ocean Model with a 3-dimentional variational data assimilation module (DA-ESROM), have been compared with the observed hydrographic and current datasets in the Ulleung Basin (UB) of the East/Japan Sea (EJS). Satellite-borne sea surface temperature and sea surface height data, and in-situ temperature profiles have been assimilated into the DA-ESROM. The performance of the DA-ESROM appears to be efficient enough to be used in an operational ocean forecast system.Comparing with the results from Mitchell et al. [Mitchell, D. A., Watts, D. R., Wimbush, M., Teague, W.J., Tracey, K. L., Book, J. W., Chang, K.-I., Suk, M.-S., Yoon, J.-H., 2005a. Upper circulation patterns in the Ulleung Basin. Deep-Sea Res. II, 52, 1617-1638.], the DA-ESROM fairly well simulates the high variability of the Ulleung Warm Eddy and Dok Cold Eddy as well as the branching of the Tsushima Warm Current in the UB. The overall root-mean-square error between 100 m temperature field reproduced by the DA-ESROM and the observed 100-dbar temperature field is 2.1 °C, and the spatially averaged grid-to-grid correlation between the two temperature fields is high with a mean value of 0.79 for the inter-comparison period.The DA-ESROM reproduces the development of strong southward North Korean Cold Current (NKCC) in summer consistent with the observational results, which is thought to be an improvement of the previous numerical models in the EJS. The reanalyzed products show that the NKCC is about 35 km wide, and flows southward along the Korean coast from spring to summer with maximum monthly mean volume transport of about 0.8 Sv in August–September.     

KERFIX, a time-series station in the Southern Ocean: a presentation

Between January 1990 and March 1995, the research project KERFIX undertook the first regular noncoastal multiyear acquisition of parameters related to the carbon cycle in the Southern Ocean at a time series station located at 50°40′ S–68°25′ E, 60 miles southwest of the Kerguelen Islands. The objectives of KERFIX are (1) to monitor the ocean/atmosphere CO₂ and O₂ exchanges and to understand which processes govern these exchanges (2) to observe and interpret the seasonal and interannual variability of the production, flux, decomposition and dissolution of carbon and associated elements at this location. In addition, micropaleontological studies describe the present and past flux dynamics in this oceanic area, to improve the knowledge of the transfer functions of some oceanographic proxies. This paper presents a survey of the KERFIX program: scientific objectives, organization of the field operations and some main results obtained since the beginning of KERFIX program, as well as the results of the temporal evolution of hydrological, chemical and biological parameters.     

Recovery of North-East Atlantic temperature fields from profiling floats: Determination of the optimal float number from sampling and instrumental error analysis

Argo is an international project that is deploying an array of temperature and salinity profiling floats over the global ocean. Here we use the error formulation derived from Optimal Statistical Interpolation to estimate statistical errors associated with the recovery of the temperature field in the North-East Atlantic ocean. Results indicate that with the present distribution of floats (119 in the considered domain), scales of wavelength larger than 500 km can be recovered with a relative uncertainty (rms error relative to the standard deviation of the field) of about 7% at 50 m, 8% at 200 m and 10% at 1000 m. This corresponds to mean absolute errors of 0.111 °C at 50 m, 0.104 °C at 200 m and 0.073 °C at 1000 m.The splitting of total errors into instrumental and sampling contributions reveals that, in the present scenario, errors are more due to the small number of floats than to instrumental errors, especially at upper levels. For scales larger than 500 km this will hold true until 200–250 floats are deployed (less than 200 for deep levels). In such a simulated scenario, the number of observations and the technology become approximately equally limiting factors for the accuracy of the temperature field mapping, with total relative errors of less than 2% at upper levels and about 3% at 1000 m.     

Assimilation of sea surface temperature in the POL Coastal Ocean Modelling System

A one-dimensional scheme is used to assimilate satellite Sea Surface Temperature data into the Proudman Oceanographic Laboratory Coastal Ocean Modelling System, set up in the Irish Sea with a fine resolution ( 1.8 km). The capabilities of the assimilation scheme are investigated using two different sets of satellite data, of lower and similar resolution to that of the model respectively. Comparison of results with independent data show that assimilation improves the modelled Sea Surface Temperature, but does not address model representation of the temperature vertical structure. It is concluded that for the Irish Sea and at the scales resolved by the model, the assimilation problem cannot be approached in a one-dimensional framework. It is also pointed out that forecast error needs to account explicitly for errors in the representation of the vertical structure of the thermal field.Three-dimensional methods that are suited for coastal systems are then suggested.     

Assimilation of SeaWiFS ocean chlorophyll data into a three-dimensional global ocean model

Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll data were assimilated with an established three-dimensional global ocean model. The assimilation improved estimates of chlorophyll relative to a free-run (no assimilation) model. Compared to SeaWiFS, annual bias of the assimilation model was 5.5%, with an uncertainty of 10.1%. The free-run model had a bias of 21.0% and an uncertainty of 65.3%. In situ data were compared to the assimilation model over a 6-year time period from 1998 through 2003, indicating a bias of 0.1%, and an uncertainty of 33.4% for daily coincident, co-located data. SeaWiFS bias was slightly higher at − 1.3% and nearly identical uncertainty at 32.7%. The free-run bias and uncertainty at − 1.4% and 61.8%, respectively, indicated how much the assimilation improved model results. Annual primary production estimates for the 1998–2003 period produced a nearly 50% improvement by the assimilation model over the free-run model as compared to a widely used algorithm using SeaWiFS chlorophyll data. These results suggest the potential of assimilation of satellite ocean chlorophyll data for improving model results.     

Remotely-sensed chlorophyll a observations of the northern Red Sea indicate seasonal variability and influence of coastal reefs

The biological dynamics of the open northern Red Sea (21.5°–27.5° N, 33.5°–40° E) have not been studied extensively, due in part to both the inaccessibility of this desert region and political considerations. Remotely-sensed chlorophyll a data therefore provide a framework to investigate the primary patterns of biological activity in this oceanic basin. Monthly chlorophyll a data from the 8-year Sea-viewing Wide Field-of-View sensor (SeaWiFS) mission, and data from the Moderate Resolution Imaging Spectroradiometer (MODIS), were analyzed with the Goddard Earth Sciences Data and Information Services Center (GES DISC) online data analysis system “Giovanni”. The data indicate that despite the normal low chlorophyll a concentrations (0.1–0.2 mg m^− 3) in these oligotrophic waters, there is a characteristic seasonal bloom in March–April in the northernmost open Red Sea (24° to 27.5° N) concurrent with minimum sea surface temperature. The location of the highest chlorophyll concentrations is consistent with a linear box model [Eshel, G., and Naik, N.H., 1997. Climatological coastal jet collision, intermediate water formation, and the general circulation of the Red Sea. J. Phys. Oceanogr. 27(7), 1233–1257.] of Red Sea circulation. Two years in the data set exhibited a different seasonal cycle consisting of a relatively weak northern spring bloom and elevated chlorophyll concentrations to the south (21.5° to 24° N).The data also indicate that large coral reef complexes may be sources of either nutrients or chlorophyll-rich detritus and sediment, enhancing chlorophyll a concentration in waters adjacent to the reefs.     

Microphytobenthos biomass at Kerguelen's Land (Subantarctic Indian Ocean): repartition and variability during austral summers

Microphytobenthos biomass has been measured at several coastal sites on the SE of the main island of the Kerguelen Archipelago (Indian Ocean), during several austral summers (1985–1992), using a conventional fluorometric method. Heterogeneity tests, conducted on two different intertidal sites (Port-Aux-Français, PAF; and Port-Raymond, PRA), showed low standard deviations, whereas the mean concentrations were highly different. Pigment concentrations showed a high variability related to the characteristics of the sediments: from low biomass in coarse intertidal sand, submitted to intense scouring (0.32±0.31 μg Chl a g⁻¹ dw, 0.29±0.14 μg Phaeo g⁻¹ dw) to high biomass in intertidal muddy sand in sheltered areas, particularly along estuaries (54 μg Chl a, 15 μg Phaeo g⁻¹ dw at Korrigan). The subtidal muddy sediments under a Macrocystis pyrifera (Linné) and Durvillaea antarctica (Chamisso in Choris) belt exhibited high concentrations in phaeopigment (Phaeo) (up to 136±83 μg g⁻¹ dw; PRA), while the concentration of chlorophyll a (Chl a) was relatively low. The dense macroalgal canopy supports an important epiphytic diatom biomass (mainly the genera Cocconeis Ehrenberg and Grammatophora Ehrenberg), which is sedimenting after degradation and is in part responsible of the high levels of Phaeo in all sediments. Macroalgal debris were observed, but diatom frustules were dominant in most surficial subtidal sediments. A circatidal mud, in the Morbihan Bay, made of a sponge spicule mat (50 m deep; 4.96 μg Chl a g⁻¹ dw), showed a very low Chl a/Phaeo ratio (0.1), while it reached up to 6 in intertidal sand. Surprisingly, a penguin rookery beach, at the east side of Courbet Peninsula, was characterized by a very low biomass (0.07±0.04 μg Chl a g⁻¹ dw), while it was nutrient enriched, particularly with nitrates.In comparison with the data at the similar latitude, but in temperate regions from the Northern Hemisphere, the microphytobenthos biomass, recorded at Kerguelen's Land, exhibited relatively high pigment concentrations, particularly the Phaeo, and supported a dense and diversified subtidal macrofauna composed of polychaetes (particularly Thelepus extensus Hutchings and Glasby), sea urchins, mytillids and gammarids. The exuberant macroalgal canopy, coastal indentations and low tidal amplitude must be in part responsible of these large benthic primary and secondary biomasses.     

Application of a SEEK filter to a 1D biogeochemical model of the Ligurian Sea: Twin experiments and real in-situ data assimilation

The Singular Evolutive Extended Kalman (SEEK) filter has been implemented to assimilate in-situ data in a 1D coupled physical-ecosystem model of the Ligurian Sea. The biogeochemical model describes the partly decoupled nitrogen and carbon cycles of the pelagic food web. The GHER hydrodynamic model (1D version) is used to represent the physical forcings. The data assimilation scheme (SEEK filter) parameterizes the error statistics by means of a set of empirical orthogonal functions (EOFs). Twin experiments are first performed with the aim to choose the suitable experimental protocol (observation and estimation vectors, number of EOFs, frequency of the assimilation,…) and to assess the SEEK filter performances. This protocol is then applied to perform real data assimilation experiments using the DYFAMED data base. By assimilating phytoplankton observations, the method has allowed to improve not only the representation of the phytoplankton community, but also of other variables such as zooplankton and bacteria that evolve with model dynamics and that are not corrected by the data assimilation scheme. The validation of the assimilation method and the improvement of model results are studied by means of suitable error measurements.