An Observing System Simulation Experiment for the Indian Ocean

Abstract

An integrated in situ Indian Ocean observing system (IndOOS) is simulated using a high-resolution ocean general circulation model (OGCM) with daily mean forcing, including an estimate of subdaily oceanic variability derived from observations. The inclusion of subdaily noise is fundamental to the results; in the mixed layer it is parameterized as Gaussian noise with an rms of 0.1°C; below the mixed layer a Gaussian interface displacement with an rms of 7 m is used. The focus of this assessment is on the ability of an IndOOS-comprising a 3°ϫ 3°Argo profiling float array, a series of frequently repeated XBT lines, and an array of moored buoys-to observe the interannual and subseasonal variability of subsurface Indian Ocean temperature. The simulated IndOOS captures much of the OGCM interannual subsurface temperature variability.A fully deployed Argo array with 10-day sampling interval is able to capture a significant part of the Indian Ocean interannual temperature variability; a 5-day sampling interval degrades its ability to capture variability. The proposed moored buoy array and frequently repeated XBT lines provide complementary information in key regions, particularly the Java/Sumatra and Somali upwelling and equatorial regions. Since the subdaily noise is of the same order as the subseasonal signal and since much of the variability is submonthly, a 5-day sampling interval does not drastically enhance the ability of Argo to capture the OGCM subseasonal variability. However, as sampling intervals are decreased, there is enhanced divergence of the Argo floats, diminished ability to quality control data, and a decreased lifetime of the floats; these factors argue against attempting to resolve subseasonal variability with Argo by shortening the sampling interval. A moored array is essential to capturing the subseasonal and near-equatorial variability in the model, and the proposed moored buoy locations span the region of strong subseasonal variability. On the whole, the proposed IndOOS significantly enhances the ability to capture both interannual and subseasonal variability in the Indian Ocean.

An observing system simulation for Southern Ocean carbon dioxide uptake

“…Critically, transport of the floats by the mean flow and eddies may affect the basin-scale monitoring of CO 2 uptake and may invalidate the assumption of a random distribution that we have used. In a high-resolution OSSE, the strong currents of the Southern Ocean affected the sampling distribution of floats in the Indian Ocean [54]. While eddies enforce a more uniform sampling distribution [48], strategies for float release, parking depth, time between profiles and ice avoidance will need to be critically evaluated for how they impact the information provided by the whole sampling array and target undersampled regions.…”

Observational Requirements of GODAE Systems

“…These include OSSEs that assess specific pre-determined design options [22,23] and techniques that objectively generate "optimal" observation arrays. The latter includes Kalman filter techniques [24], ensemble approaches [25] and adjoint and representer-based methods [26,27,28]. Some of the studies referred to above have contributed to the design to assessment of the Argo array; some have assessed the design of tropical mooring arrays; and others have identified regions that may help constrain model variability in western boundary currents.…”

Simulating biomass assimilation in a Mediterranean ecosystem model using SOFA: setup and identical twin experiments

“…These climatological profiles do maintain a strong stability in the water column during the seasonal cycles (Zavatarelli et al, 1998;Fig. 11).…”

“…OSSEs are used for two distinct objectives: to define the best observational network for improving forecasting; to determine the impact of an hypothetical dataset upon a simulated system. The result of the OSSE is in both cases the measure of the distance of the ecosystem evolving with or without the data network under analysis Vecchi and Harrison, 2006). This kind of methodology has been performed by means of the singular evolutive extended Kalman filter and of the ensemble variant of this method respectively in the onedimensional ecosystem model and in the three-dimensional one of the Cretan Sea (Triantafyllou et al, 2005).…”