Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

[摘要] In the context of stand alone ocean models,the atmospheric forcing is generally computedusing atmospheric parameters that are derivedfrom atmospheric reanalysis data and/or satellite products.With such a forcing, the sea surface temperaturethat is simulated by the ocean model is usuallysignificantly less accurate than the synoptic mapsthat can be obtained from the satellite observations.This not only penalizes the realism of the oceanlong-term simulations, but also the accuracy of the reanalysesor the usefulness of the short-term operational forecasts(which are key GODAE and MERSEA objectives).In order to improve the situation, partly resultingfrom inaccuracies in the atmospheric forcing parameters,the purpose of this paper is to investigate a wayof further adjusting the state of the atmosphere(within appropriate error bars), so that an explicitocean model can produce a sea surface temperaturethat better fits the available observations.This is done by performing idealized assimilationexperiments in which Mercator-Ocean reanalysis dataare considered as a reference simulationdescribing the true state of the ocean.Synthetic observation datasets for sea surface temperatureand salinity are extracted from the reanalysisto be assimilated in a low resolution global ocean model.The results of these experiments show that it is possibleto compute piecewise constant parameter corrections,with predefined amplitude limitations, so that long-termfree model simulationsbecome much closer to the reanalysis data,with misfit variance typically divided by a factor 3.These results are obtained by applying a Monte Carlomethod to simulate the joint parameter/stateprior probability distribution.A truncated Gaussian assumption is usedto avoid the most extreme and non-physical parameter corrections.The general lesson of our experiments is indeedthat a careful specification of the prior informationon the parameters and on their associated uncertaintiesis a key element in the computation of realisticparameter estimates, especially if the system is affectedby other potential sources of model errors.

[学科分类] 海洋学与技术