Improving the parameters of a global ocean biogeochemical model via variational assimilation of in situ data at five time series stations

[1] The global ocean biogeochemical models that are used in order to assess the ocean role in the global carbon cycle and estimate the impact of the climate change on marine ecosystems are getting more and more sophisticated. They now often account for several phytoplankton functional types that play particular roles in marine food webs and the ocean carbon cycle. These phytoplankton functional types have specific physiological characteristics, which are usually poorly known and therefore add uncertainties to model results. Indeed, this evolution in model complexity is not accompanied by a similar increase in the number and diversity of in situ data sets necessary for model calibration and evaluation. Thus, it is of primary importance to develop new methods to improve model performance using existing biogeochemical data sets, despite their current limitations. In this paper, we have optimized 45 physiological parameters of the PISCES global model, using a variational optimal control method. In order to bypass a global 3-D ocean variational assimilation, which would require enormous computation and memory storage, we have simplified the estimation procedure by assimilating monthly climatological in situ observations at five contrasted oceanographic stations of the JGOFS program in a 1-D version of the PISCES model. We began by estimating the weight matrix in the cost function by using heuristic considerations. Then we used this matrix to estimate the 45 parameters of the 1-D version of the PISCES model by assimilating the different monthly profiles (observed profiles at the five stations) in the same variational procedure on a time window of 1 year. This set of optimized parameters was then used in the standard 3-D global PISCES version to perform a 500 year global simulation. The results of both the standard and the optimized versions of the model were compared to satellite-derived chlorophyll-a images, which are an independent and global data set, showing that our approach leads to significant improvements in simulated surface chlorophyll-a in most of the regions of the world ocean. Besides demonstrating that we have improved the accuracy of the PISCES model, this study proposes a sound methodology that could be used to efficiently account for in situ data in biogeochemical ocean models.