Abstract
Abstract. Today, the routine assimilation of satellite data into operational models of ocean circulation is mature enough to enable the production of global reanalyses describing the ocean circulation variability during the past decades. The expansion of the "reanalysis" concept from ocean physics to biogeochemistry is a timely challenge that motivates the present study. The objective of this paper is to investigate the potential benefits of assimilating satellite-estimated chlorophyll data into a basin-scale three-dimensional coupled physical–biogeochemical model of the North Atlantic. The aim is on the one hand to improve forecasts of ocean biogeochemical properties and on the other hand to define a methodology for producing data-driven climatologies based on coupled physical–biogeochemical modeling. A simplified variant of the Kalman filter is used to assimilate ocean color data during a 9-year period. In this frame, two experiments are carried out, with and without anamorphic transformations of the state vector variables. Data assimilation efficiency is assessed with respect to the assimilated data set, nitrate of the World Ocean Atlas database and a derived climatology. Along the simulation period, the non-linear assimilation scheme clearly improves the surface analysis and forecast chlorophyll concentrations, especially in the North Atlantic bloom region. Nitrate concentration forecasts are also improved thanks to the assimilation of ocean color data while this improvement is limited to the upper layer of the water column, in agreement with recent related literature. This feature is explained by the weak correlation taken into account by the assimilation between surface phytoplankton and nitrate concentrations deeper than 50 meters. The assessment of the non-linear assimilation experiments indicates that the proposed methodology provides the skeleton of an assimilative system suitable for reanalyzing the ocean biogeochemistry based on ocean color data.
Highlights
Monitoring the evolution of the marine biogeochemistry with relevant accuracy and resolution is a key requirement to better understand the ocean response to accelerating global climate change and the consequent effects on the carbon cycle and living resources
The performances of the assimilative system are first evaluated in terms of ocean surface properties, before investigating how the assimilated ocean color data modify the distribution of nutrients in the sub-surface layers
The simulations were performed with a fixed variant of the reduced-rank Kalman filter (SEEK) to limit the computational burden of the assimilation process
Summary
Monitoring the evolution of the marine biogeochemistry with relevant accuracy and resolution is a key requirement to better understand the ocean response to accelerating global climate change and the consequent effects on the carbon cycle and living resources. While it is conceivable to characterize the biogeochemical properties of a limited zone in the coastal domain through field measurements only (oceanographic cruises, autonomous sensors, etc.), it seems unrealistic to obtain spatially and temporally synoptic descriptions of vast ocean basins using similar approaches in the foreseeable future. Spatial ocean color sensors are the main source of global biogeochemical data available today. These sensors enable the observation of optical properties of the upper ocean such as the water leaving radiance in the visible spectrum, which can be related to the sea surface chlorophyll concentration
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