Integrating biogeochemistry and ecology into ocean data assimilation systems

Monitoring and predicting the biogeochemical state of the ocean and marine ecosystems is an important application of operational oceanography that needs to be expanded. The accurate depiction of the ocean's physical environment enabled by Global Ocean Data Assimilation Experiment (GODAE) systems, in both real-time and reanalysis modes, is already valuable for various for various applications, such as the fishing industry and fisheries management. However, most of these applications require accurate estimates of both physical and biogeochemical ocean conditions over a wide range of spatial and temporal scales. In this paper, we discuss recent developments that enable coupling new biogeochemical models and assimilation components with the existing GODAE systems, and we examine the potential of such systems in several areas of interest: phytoplankton biomass monitoring in the open ocean, ocean carbon cycle monitoring and assessment, marine ecosystem management at seasonal and longer time scales, and downscaling in coastal areas. A number of key requirements and research priorities are then identified for the future, GODAE systems will need to improve their representation of physical variables that are not yet considered essential, such as upper-ocean vertical fluxes that are critically important to biological activity. Further, the observing systems will need to be expanded in terms of in situ platforms (with intensified deployments of sensors for O-2 and chlorophyll, and inclusion of new sensors for nutrients, zooplankton, micronekton biomass, and others), satellite missions (e.g., hyperspectral instruments for ocean color, lidar systems for mixed-layer depths, and wide-swath altimeters for coastal sea level), and improved methods to assimilate these new measurements

Stochastic estimation of biogeochemical parameters from Globcolour ocean color satellite data in a North Atlantic 3D ocean coupled physical-biogeochemical model

Biogeochemical parameters remain a major source of uncertainty in coupled physical-biogeochemical models of the ocean. In a previous study (Doron et al., 2011), a stochastic estimation method was developed to estimate a subset of biogeochemical model parameters from surface phytoplankton observations. The concept was tested in the context of idealized twin experiments performed with a 1/4° resolution model of the North Atlantic ocean. The method was based on ensemble simulations describing the model response to parameter uncertainty. The statistical estimation process relies on nonlinear transformations of the estimated space to cope with the non-Gaussian behaviour of the resulting joint probability distribution of the model state variables and parameters. In the present study, the same method is applied to real ocean colour observations, as delivered by the sensors SeaWiFS, MERIS and MODIS embarked on the satellites OrbView-2, Envisat and Aqua respectively. The main outcome of the present experiments is a set of regionalised biogeochemical parameters. The benefit is quantitatively assessed with an objective norm of the misfits, which automatically adapts to the different ecological regions. The chlorophyll concentration simulated by the model with this set of optimally derived parameters is closer to the observations than the reference simulation using uniform values of the parameters. In addition, the interannual and seasonal robustness of the estimated parameters is tested by repeating the same analysis using ocean colour observations from several months and several years. The results show the overall consistency of the ensemble of estimated parameters, which are also compared to the results of an independent study