A current stake for numerical ocean models is to adequately represent meso- and small-scale activity, in order to simulate its crucial role in the general ocean circulation and energy budget. It is therefore also a challenge for data assimilation (DA) methods to control these scales. However this small-scale activity is strongly linked to the nonlinear character of the flow, whereas DA methods are generally much less efficient in such contexts than in (almost) linear ones. The purpose of this poster is to address this problem specifically, by exploring the behaviour of an incremental 4D-VAR DA method in a nonlinear ocean model. A series of experiments assimilating simulated altimeter data in an idealized Gulfstream-like configuration of the NEMO ocean model at increasing resolutions (which is a proxy for increasing nonlinearity) are analyzed. We present in particular results characterizing scales and structures of the analysis error along the assimilation process, as well as tentative links with small scale activity. Moreover we investigate some strategies for DA in such nonlinear contexts, with the aim of reducing this analysis error.