Assessment of the impact of TS assimilation from ARGO floats in the Mediterranean Sea

Griffa, Annalisa; Molcard, Anne; Raicich, Fabio; Rupolo, V.

doi:10.5194/os-2-237-2006

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…The technique has been used for the analysis of different ocean observing systems in ocean models of varying complexity [Kindle, 1986;Barth and Wunsch, 1990;Bennett, 1990;Hernandez et al, 1995;Hackert et al, 1998]. For the Argo array, OSSEs concentrated on the Indian Ocean [Schiller et al, 2004;Oke and Schiller, 2007;Ballabrera-Poy et al, 2007;Vecchi and Harrison, 2007] and the Mediterranean Sea [Griffa et al, 2006]. The OSSE approach has several advantages.…”

Section: Observing System Simulation Experimentsmentioning

confidence: 99%

“…Vecchi and Harrison [2007] demonstrate that more frequent sampling decreases the accuracy of the Argo system in the tropics because of the increased time spent by the floats at the surface and the resulting enhanced divergence of the floats. The OSSE approach also permits calculation of the optimal float distribution and trajectories [Oke and Schiller, 2007;Griffa et al, 2006].…”

Section: Observing System Simulation Experimentsmentioning

confidence: 99%

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Simulation of the Argo observing system in an ocean general circulation model

et al. 2009

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[1] The main goal of this study is to determine how well large-scale temperature, salinity, upper ocean heat content (UOHC), and surface mixed layer fields and their variability can be reconstructed from the Argo observing system. The approach is to sample and reconstruct these oceanic fields from a coarse-resolution ocean general circulation model (OGCM), quantify the errors in the reconstructed fields, and analyze the factors controlling these errors. In particular, this study analyzes the effects of float movements on the spatial coverage and reconstruction of temporal variability. Overall performance of the simulated Argo array is good, and the reconstructed climatological means of such key quantities as the temperature, salinity, UOHC, and mixed layer depth are very close to the actual OGCM-simulated values in most of the global ocean. However, the differences between the reconstructed and actual fields (''reconstruction errors'') are more significant in several regions with strong currents, such as the Antarctic Circumpolar Current (ACC). The results also suggest that the detection of the year-to-year changes in UOHC in the ACC, in high-latitude North Atlantic, and near the coasts can be particularly problematic. As illustrated by sensitivity experiments, the main effect of float movements is to increase reconstruction errors. This adverse effect of float movements is the main cause of large errors in the UOHC interannual difference in the ACC. When the spatial sampling coverage is improved, for example, by increasing the number of floats, the accuracy of reconstruction improves substantially.

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Section: Observing System Simulation Experimentsmentioning

confidence: 99%

Section: Observing System Simulation Experimentsmentioning

confidence: 99%

Simulation of the Argo observing system in an ocean general circulation model

et al. 2009

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“…The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project: Technologies, Methods, and Early Results 1986; Barth and Wunsch, 1990;Bennett, 1990;Hernandez et al, 1995;Hackert et al, 1998;Schiller et al, 2004;Ballabrera-Poy et al, 2007;Vecchi and Harrison, 2007;Griffa et al, 2006]. In the setting relevant to SOCCOM, float trajectories can be either simulated in a numerical model or taken from an existing profiling float array (Argo, http://www.argo.ucsd.edu) and then used to sample a model-simulated field the way that the real floats sample the real ocean.…”

Section: Special Sectionmentioning

confidence: 99%

Observing System Simulation Experiments for an array of autonomous biogeochemical profiling floats in the Southern Ocean

et al. 2017

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This study uses Observing System Simulation Experiments (OSSEs) to examine the reconstruction of biogeochemical variables in the Southern Ocean from an array of autonomous profiling floats. In these OSSEs, designed to be relevant to the Southern Ocean Carbon and Climate Observation and Modeling (SOCCOM) project, the simulated floats move with oceanic currents and sample dissolved oxygen and inorganic carbon. The annual mean and seasonal cycle of these fields are then reconstructed and compared to the original model fields. The reconstruction skill is quantified with the reconstruction error (RErr), defined as the difference between the reconstructed and actual model fields, weighted by a local measure of the spatiotemporal variability. The square of the RErr is small (<0.5) for 150 floats in most of the domain, which is interpreted to mean that the reconstruction skill is high. An idealized analytical study demonstrates that the RErr depends on the magnitude of the seasonal cycle, spatial gradients, speed of float movement, amplitude of mesoscale variability, and number of floats. These factors explain a large part of the spatial variability in the RErr and can be used to predict the reconstruction skill of the SOCCOM array. Furthermore, our results demonstrate that an array size of 150 floats is a reasonable choice for reconstruction of surface properties and annual‐mean 2000 m inventories, with the exception of the seasonal cycle in parts of the Indo‐Atlantic, and that doubling this number to 300 results in a very modest increase in the reconstruction skill for dissolved oxygen.

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“…Observing System [Simulation] Experiments (OS[S]Es; Fujii et al, 2019) are the most common computational tools in oceanography to support observing system design (e.g., Balmaseda et al, 2007;Gasparin et al, 2019;Griffa et al, 2006;Halliwell et al, 2017). OSEs are limited to evaluate existing observing systems, whereas OSSEs can test the skill of proposed future observing systems.…”

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confidence: 99%

Leveraging Uncertainty Quantification to Design Ocean Climate Observing Systems

Loose

Heimbach

2021

J Adv Model Earth Syst

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Sustaining long-term ocean observations to develop climate-quality observational records is crucial for understanding the ocean's role in climate and for evaluating climate model simulations (National Academies of Sciences, Engineering, and Medicine, 2017). Yet, ocean observing faces multiple challenges: complex deployment operations in frequently rough weather (or ice) conditions, limited instrument lifetime due to corrosive and high-pressure environments, and the necessity of adequate spatial and temporal sampling. The high cost and logistical challenges call for deliberate, quantitative approaches. Here, we leverage adjoint modeling and Hessian uncertainty quantification (UQ) within the ECCO (Estimating the Circulation and Climate of the Ocean) framework to explore a new design strategy for ocean climate observing systems. This approach has two distinguishing features, which, taken together, foster collaboration and system

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Assessment of the impact of TS assimilation from ARGO floats in the Mediterranean Sea

Cited by 8 publications

References 26 publications

Simulation of the Argo observing system in an ocean general circulation model

Simulation of the Argo observing system in an ocean general circulation model

Observing System Simulation Experiments for an array of autonomous biogeochemical profiling floats in the Southern Ocean

Leveraging Uncertainty Quantification to Design Ocean Climate Observing Systems

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