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Abstract. Traditionally, in order for lower-resolution, global- or basin-scale(regional) models to benefit from some of the improvements available inhigher-resolution subregional or coastal models, two-way nesting has to beused. This implies that the parent and child models have to be run togetherand there is an online exchange of information between both models. Thisapproach is often impossible in operational systems where different modelcodes are run by different institutions, often in different countries.Therefore, in practice, these systems use one-way nesting with data transferonly from the parent model to the child models. In this article, it isexamined whether it is possible to replace the missing feedback (coming fromthe child model) by data assimilation, avoiding the need to run the modelssimultaneously. Selected variables from the high-resolution simulation willbe used as pseudo-observations and assimilated into the low-resolution models.This method will be called “upscaling”. A realistic test case is set up with a model covering the Mediterranean Sea,and a nested model covering its north-western basin. Under thehypothesis that the nested model has better prediction skills than the parentmodel, the upscaling method is implemented. Two simulations of the parentmodel are then compared: the case of one-way nesting (or a stand-alone model)and a simulation using the upscaling technique on the temperature andsalinity variables. It is shown that the representation of some processes,such as the Rhone River plume, is strongly improved in the upscaled modelcompared to the stand-alone model.

Upscaling of a local model into a larger-scale model