Abstract
Abstract. Assessments of ocean data assimilation (DA) systems and observing system design experiments typically rely on identical or nonidentical twin experiments. The identical twin approach has been recognized as yielding biased impact assessments in atmospheric predictions, but these shortcomings are not sufficiently appreciated for oceanic DA applications. Here we present the first direct comparison of the nonidentical and identical twin approaches in an ocean DA application. We assess the assimilation impact for both approaches in a DA system for the Gulf of Mexico that uses the ensemble Kalman filter. Our comparisons show that, despite a reasonable error growth rate in both approaches, the identical twin produces a biased skill assessment, overestimating the improvement from assimilating sea surface height and sea surface temperature observations while underestimating the value of assimilating temperature and salinity profiles. Such biases can lead to an undervaluation of some observing assets (in this case profilers) and thus a misguided distribution of observing system investments.
Highlights
Ocean data assimilation (DA), i.e., the incorporation of observations into ocean models to obtain the best possible estimate of the ocean state, has become standard practice for improving the accuracy of model predictions and reanalyses
This meets the requirement suggested by Halliwell et al (2014) that the errors between the free run and the truth should grow at a similar rate as errors that develop between state-of-the-art ocean models and the true ocean
Ble between the nonidentical and identical twin experiments. This satisfies the other requirements suggested in Halliwell et al (2014), namely that the free run is able to reproduce the main features of the simulated phenomenon with some realism and that there are sufficient differences between the free and truth runs for the assimilation method to correct
Summary
Ocean data assimilation (DA), i.e., the incorporation of observations into ocean models to obtain the best possible estimate of the ocean state, has become standard practice for improving the accuracy of model predictions and reanalyses. It is straightforward to assess the assimilation impact (i.e., the differences between ocean state estimates from a model run with and without assimilation), whereby a better fit of the model state to observations following assimilation might be considered positive. The essential steps of a twin experiment are to (1) predefine a simulation as the “truth”, (2) sample synthetic observations from this truth, (3) assimilate these observations into a different simulation referred to as the forecast run, and (4) assess the skill of this assimilative run against a non-assimilative (“free”) run using independent observations sampled from the truth. If the chosen truth and forecast runs are from same model implementation but with perturbed initial, forcing or boundary conditions, the method is referred to as the “identical twin” approach. If two different model types are used, we refer to the method as the “nonidentical twin” approach. We note that the intermediate approach in which the same model type is employed but with sufficiently different con-
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