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

Abstract

[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.

An Ensemble Observing System Simulation Experiment of Global Ocean Heat Content Variability

“…The study of Kamenkovich et al (2009) is perhaps the most similar to the present one. They simulated the Argo observing system (by sampling randomly with a specified number of locations or by simulating float trajectories) in an ocean general cir-25 culation model with increasing CO 2 forcing for years 1992 through 2001, which were then converted to global fields using the objective analysis procedure of Mariano and Brown (1992).…”

“…While this modeling system has been shown to have skill in reproducing large-scale features and variability of the climate system, mesoscale and synoptic 30 variability are underestimated or neglected. Furthermore, unlike the study of Kamenkovich et al (2009) where Argo drifters are actually simulated as advective trajectories using the modeled data, here we preserve the locations and times of the actual observing system. Preserving these locations simplifies our ensemble experiment, but prevents a determination of the biases induced by trajectory-climate correlations.…”

“…Synthetic observations are taken from each model segment by linearly interpolating the model data in latitude, longitude and time to the latitudes, longitudes, and times of the observations in the CORA dataset, and using the nearest-neighbor model We thus use the locations of the actual drifters to make synthetic measurements, which differs from the technique used by Kamenkovich et al (2009), where synthetic drifters were advected by the model flow.…”

Rigorous Evaluation of a Fraternal Twin Ocean OSSE System for the Open Gulf of Mexico

“…Most published ocean OSSE studies did not use a full-fledged DAS for assimilation. Instead, field reconstruction techniques were often used, specifically generating two-or three-dimensional maps from synthetic observations using procedures such as multivariate optimum interpolation, Kalman filter interpolation, or projection onto dominant empirical orthogonal functions (e.g., Oke and Schillera 2007a;Vecchi and Harrison, 2007;Ballabrera-Poy et al 2007;Sakov and Oke 2008;Kamenkovich et al 2009). Studies by Guinehut et al (2002Guinehut et al ( , 2004 used the field reconstruction approach, but they did not specifically refer to their approach as an OSSE.…”

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

“…Kamenkovich et al . [] discussed “positive” (increase in the sampling coverage) and “adverse” (distortion of the seasonal cycle) effects of float movement. The positive effect explains the decrease in RErr a with increasing U (Figure a).…”