Abstract. Injection of carbon dioxide (CO2) in the ocean has been proposed as an option for accelerating the natural net flux of CO2 from the atmosphere into the ocean. Liquid CO2 released as droplets at depths <3000 m will create an ascending plume of droplets and entrained water. As the CO2 droplets dissolve, carbon is transferred into the plume water, yielding increased density and a lowered pH value of the plume water. As ambient water entrains the CO2-enriched water by mixing, the density difference disappears and the injected CO2 follows the ocean dynamics as a dynamically passive tracer. Here we report on numerical experiments performed with a two-phase Navier Stokes solver. The effects of different droplets sizes, background currents, and injection rates are examined. The numerical experiments show that the droplet size and the background current are key parameter for predicting the vertical distribution of the plume water, the associated reduction in the pH field, and the increase in the plume water density. If rapid dilution of the CO2-enriched water is the objective (leading to modest reduction in the pH value), large initial droplets and high background currents are preferable. On the other hand, if the objective is to increase the density of the plume water in order to generate a sinking plume (yielding enhanced residence time of the released CO2), CO2 injection with small droplets in a stagnant water column is optimal.

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Abstract. The natural ocean uptake of the greenhouse gas CO•. can be accelerated by collecting and liquefying the gas from point sources, and by pumping it into the ocean at appropriate locations and at suificient depths. Results from a numerical modelling system indicate that injection sites located at about 1,000 m depth in the eastern Norwegian Sea lead to eificient and long term sequestration in the abyss Atlantic. For a release rate corresponding to the CO•. emissions from a 220 MW gas power plant, it is found that the volume of the near-source water with a pH-reduction •_ 0.1 is --0.5 km 3. These findings, together with available technol-

Reaction rates (fluxes) in a metabolic network can be analyzed using constraint-based modeling which imposes a steady state assumption on the system. In a deterministic formulation of the problem the steady state assumption has to be fulfilled exactly, and the observed fluxes are included in the model without accounting for experimental noise. One can relax the steady state constraint, and also include experimental noise in the model, through a stochastic formulation of the problem. Uniform sampling of fluxes, feasible in both the deterministic and stochastic formulation, can provide us with statistical properties of the metabolic network, such as marginal flux probability distributions. In this study we give an overview of both the deterministic and stochastic formulation of the problem, and of available Monte Carlo sampling methods for sampling the corresponding solution space. We apply the ACHR, OPTGP, CHRR and Gibbs sampling algorithms to ten metabolic networks and evaluate their convergence, consistency and efficiency. The coordinate hit-and-run with rounding (CHRR) is found to perform best among the algorithms suitable for the deterministic formulation. A desirable property of CHRR is its guaranteed distributional convergence. Among the three other algorithms, ACHR has the largest consistency with CHRR for genome scale models. For the stochastic formulation, the Gibbs sampler is the only method appropriate for sampling at genome scale. However, our analysis ranks it as less efficient than the samplers used for the deterministic formulation.

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