Description: The Joint Global Ocean Flux Study (JGOFS) has completed a decade of intensive process and time-series studies on the regional and temporal dynamics of biogeochemical processes in five diverse ocean basins. Its field program also included a global survey of dissolved inorganic carbon (DIC) in the ocean, including estimates of the exchange of carbon dioxide (CO2) between the ocean and the atmosphere, in cooperation with the World Ocean Circulation Experiment (WOCE). This report describes the principal achievements of JGOFS in ocean observations, technology development and modelling. The study has produced a comprehensive and high-quality database of measurements of ocean biogeochemical properties. Data on temporal and spatial changes in primary production and CO2 exchange, the dynamics of of marine food webs, and the availability of micronutrients have yielded new insights into what governs ocean productivity, carbon cycling and export into the deep ocean, the set of processes collectively known as the "biological pump." With large-scale, high-quality data sets for the partial pressure of CO2 in surface waters as well for other DIC parameters in the ocean and trace gases in the atmosphere, reliable estimates, maps and simulations of air-sea gas flux, anthropogenic carbon and inorganic carbon export are now available. JGOFS scientists have also obtained new insights into the export flux of particulate and dissolved organic carbon (POC and DOG), the variations that occur in the ratio of elements in organic matter, and the utilization and remineralization of organic matter as it falls through the ocean interior to the sediments. JGOFS scientists have amassed long-term data on temporal variability in the exchange of CO2 between the ocean and atmosphere, ecosystem dynamics, and carbon export in the oligotrophic subtropical gyres. They have documented strong links between these variables and large-scale climate patterns such as the El Nino-Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO). An increase in the abundance of organisms that fix free nitrogen (N-2) and a shift in nutrient limitation from nitrogen to phosphorus in the subtropical North Pacific provide evidence of the effects of a decade of strong El Ninos on ecosystem structure and nutrient dynamics. High-quality data sets, including ocean-color observations from satellites, have helped modellers make great strides in their ability to simulate the biogeochemical and physical constraints on the ocean carbon cycle and to extend their results from the local to the regional and global scales. Ocean carbon-cycle models, when coupled to atmospheric and terrestrial models, will make it possible in the future to predict ways in which land and ocean ecosystems might respond to changes in climate.

Description: Observations from cruises in the Arabian Sea and data from satellites are interpreted using different realizations of a multi-level primitive equation model and an eddy-permitting reduced-gravity shallow water model of the Indian Ocean. The focus is on the interannual circulation variability of the Arabian Sea, and especially of the meridional location of the Great Whirl (GW). The results suggest that the variability in the western Arabian Sea is not only due to the interannual variability in the wind field, but that a substantial part is caused by the chaotic nature of the ocean dynamics. Decreasing the friction coefficient from 1000 to 500m2s-1 in a 19o numerical reduced-gravity model, the variance of the GW location increases dramatically, and the mean position moves southward by one degree. In the eddy-permitting experiments analyzed, both mechanisms appear to determine the GW location at the onset of the GW dynamics in late summer.

Description: Assimilation experiments with data from the Bermuda Atlantic Time-series Study (BATS, 1989¯1993) were performed with a simple mixed-layer ecosystem model of dissolvedinorganic nitrogen (N), phytoplankton (P) and herbivorous zooplankton (H). Our aim is to optimize the biological model parameters, such that the misfits between model results andobservations are minimized. The utilized assimilation method is the variational adjoint technique, starting from a wide range of first-parameter guesses. A twin experiment displayedtwo kinds of solutions, when Gaussian noise was added to the model-generated data. The expected solution refers to the global minimum of the misfit model-data function, whereasthe other solution is biologically implausible and is associated with a local minimum. Experiments with real data showed either bottom-up or top-down controlled ecosystemdynamics, depending on the deep nutrient availability. To confine the solutions, an additional constraint on zooplankton biomass was added to the optimization procedure. Thisinclusion did not produce optimal model results that were consistent with observations. The modelled zooplankton biomass still exceeded the observations. From the model-datadiscrepancies systematic model errors could be determined, in particular when the chlorophyll concentration started to decline before primary production reached its maximum. Adirect comparision of measured 14C-production data with modelled phytoplankton production rates is inadequate at BATS, at least when a constant carbon to nitrogen C : N ratio isassumed for data assimilation.

Description: A first step for improving the climatological state of high‐resolution general circulation models by means of data assimilation is presented. A method developed for the assimilation of statistical characteristics into chaotic ocean models is applied to assimilate SSH variability from TOPEX/POSEIDON and ERS1 in association with temperature and salinity from the World Ocean Atlas 1997 in order to estimate the underlying mean circulation. The method requires a parameterization of SSH variability which derives from the approach of Green and Stone. By estimating initial conditions for temperature and salinity, a mean state is achieved which, although not fully consistent with the altimetric and climatological data, is markedly improved on time scales of one year in comparison to the control run. The assimilation of SSH variability data introduces complementary information about the main frontal structures consistent with climatological observations. The state is however not an equilibrium state and returns back to the first guess quasi‐equilibrium state for longer integration periods.

Description: The study investigates perspectives of the parameter estimation problem with the adjoint method in eddy-resolving models. Sensitivity to initial conditions resulting from the chaotic nature of this type of model limits the direct application of the adjoint method by predictability. Prolonging the period of assimilation is accompanied by the appearance of an increasing number of secondary minima of the cost function that prevents the convergence of this method. In the framework of the Lorenz model it is shown that averaged quantities are suitable for describing invariant properties, and that secondary minima are for this type of data transformed into stochastic deviations. An adjoint method suitable for the assimilation of statistical characteristics of data and applicable on time scales beyond the predictability limit is presented. The approach assumes a greater predictability for averaged quantities. The adjoint to a prognostic model for statistical moments is employed for calculating cost function gradients that ignore the fine structure resulting from secondary minima. Coarse resolution versions of eddy-resolving models are used for this purpose. Identical twin experiments are performed with a quasigeostrophic model to evaluate the performance and limitations of this approach in improving models by estimating parameters. The wind stress curl is estimated from a simulated mean stream function. A very simple parameterization scheme for the assimilation of second-order moments is shown to permit the estimation of gradients that perform efficiently in minimizing cost functions.