## 1. Introduction

[2] Addressing the need of the oceanographic community for global, continuous profiling of the ice free upper ocean, the deployment of the Argo array target of 3000 floats was reached in 2007. The floats spend most of the time at a depth of 1000–2000 m, where they are advected by the deep oceanic currents. Every 10 days, the floats surface and take vertical profiles of temperature and salinity over the depth interval from 1000 to 2000 m to the surface. The resulting three-dimensional global data set is unprecedented in the history of oceanographic observations. For the first time, reliable estimates of the global upper ocean stratification are available, which will significantly advance understanding of the state of the oceans and enhance the ability to predict future climate changes. In particular, data for temperature and salinity help to constrain climate models used for climate change projections, whereas the information contained in such derived variables as the upper ocean heat content (UOHC) and mixed layer depth (MLD) is central for the ability to detect ongoing climate changes.

[3] Several factors, however, impact the ability of the Argo observing system to accurately reconstruct the upper oceanic state, and an analysis of such factors is crucial for the understanding of the limitations of the Argo system. Coarse spatial and temporal sampling is an important limiting factor. If the 3000 floats are nearly uniformly distributed, the average distance between floats is approximately 300 km. Such coarse spatial resolution is insufficient to resolve sharp oceanic fronts associated with western boundary currents, and the Argo array was never intended to provide accurate measurements in these areas. The Antarctic Circumpolar Current (ACC) represents another region with strong currents and high gradients, and the ability of the Argo to reconstruct temperature and salinity structure there is an important uncertainty in the Argo-based reconstruction of the global oceanic state. This paper addresses the significance of the differences between the reconstructed and actual values of some state estimates and examines the effects of float movements and spatial sampling coverage on these biases. The goal of this study is to examine how well temperature, salinity, UOHC and surface mixed layer fields and their variability can be reconstructed from the Argo data.

### 1.1. Effects of Advection

[4] Movement of the Argo floats by oceanic currents has complicated effects on the overall accuracy of reconstruction of the gridded fields from the Argo data. In particular, the constant redistribution of floats on average acts to increase the spatial sampling coverage of the Argo system, by providing observations from more points in the domain, and can thus be expected to improve the Argo ability to reconstruct oceanic fields. In the regions with divergent currents, on the other hand, the spatial sampling coverage can be expected to decrease [*Vecchi and Harrison*, 2007]. The float redistribution can also negatively impact the reconstruction of the time variability in sampled fields, by decreasing the time a float spends near any particular location.

[5] These competing “positive” and “adverse” effects can be illustrated on the example of a one-dimensional, uniform current. The density of the sampling coverage, in this case, can be measured by the length scale *d* within which at least one sampling is guaranteed during a time interval *T*. If the floats are not moving, *d* = *D*, where *D* is the spacing between floats (300 km). If the floats are advected by a current of speed *U* over time *T*, *d* is reduced to *D* − *UT*. Long advective length scale *UT* leads to increased spatial sampling coverage (smaller *d*), and for *UT* ∼ 300 km, the advection-induced improvement in the sampling coverage becomes very significant. Since the resolution of the annual cycle is usually a minimum requirement for reconstruction of climatology and variability of oceanic variables, *T* is taken to be a monthly time scale, 30 days. Then for the slow advection of *U* < 0.01 m s^{−1}, typical for most large-scale subsurface currents in the oceanic interior, *d* is very close to *D* and the spatial sampling coverage is largely unaffected by advection. Moreover, the time mean velocities are directed primarily along mean isobars, and the advection does not affect important cross-gradient resolution. Positive effects of advection become noticeable at approximately *U* > 0.03 m s^{−1}; and for the fast advection of *U* ∼ 0.1 m s^{−1}*d* becomes approximately 40 km. Note, however, that the required spatial resolution should be in general proportional to the characteristic length scale *L* on which temperature and salinity change. Such length scale is typically shorter in the regions of strong geostrophic currents, which limits the positive effect of advection.

[6] The importance of the adverse affects of advection can be quantified by estimating an advective time scale *L*/*U*. On this time scale, float movements cause noticeable changes in the sampled fields, through changing accuracy of reconstruction at any fixed location. If the time scale *L*/*U* is comparable to the time scale of interest, the movement of floats can severely distort the reconstructed variability. The advective time scale *L*/*U* is approximately 580 days for *U* ∼ 0.01 m s^{−1} and *L* ∼ 500 km (typical scales for oceanic interior), and 23 days for *U* ∼ 0.05 m s^{−1} and *L* ∼ 100 km (typical scales for the ACC). On the basis of these estimates, significant distortion of the annual cycle is expected in regions of strong current and short characteristic length scale, such as the ACC and boundary current regions; whereas in the interior of midlatitude gyres, the adverse effects of float movements are expected to be felt on interannual time scales only. Finally, even relatively weak (*U* ∼ 0.002 m s^{−1}) but divergent flows can cause significant gaps in spatial coverage over the course of 5 years (*L* ∼ 300 km).

### 1.2. Observing System Simulation Experiments

[7] To estimate the error in the fields reconstructed from Argo data, a simulation of the Argo sampling is studied here using an oceanic general circulation model (OGCM), in an Observing System Simulation Experiment (OSSE) [*Arnold and Dey*, 1986]. 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. Most importantly, the actual field is known and thus the errors in the reconstructed fields can be accurately estimated. Additionally, parameters of the observing system and the “observed” oceanic state can be modified in sensitivity studies and the affects of various factors on the accuracy of the observing system can be isolated and estimated. For example, *Schiller et al.* [2004] demonstrate the importance of spatial resolution for capturing intraseasonal oscillations in the upper Indian Ocean, and recommend a meridional resolution of 100 km in the equatorial region. *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].

[8] This study makes a first attempt to analyze the Argo system in global OSSEs and to focus on the effects of float movements and spatial sampling coverage on the ability of the Argo system to reconstruct oceanic state variables. It can be argued that, due its coarse spatial resolution, the main objective of the Argo array is to measure large-scale fields. This study evaluates to what degree this objective can be met and utilizes a coarse-resolution ocean model in order to analyze the reconstruction of large-scale oceanic features. All the advection is, however, carried by large-scale currents and most importantly, the mesoscale eddies are absent. These idealized simulations do not attempt to account for the actual launching times and locations, difference in sampling times, differences in float design and finite ascending/descending times. In addition, the pressure offset error, and the effects of the loss of floats due to mechanical failures and vandalism are also ignored. Given all these idealizations, this study is expected to provide an upper bound estimate on the accuracy of the Argo observing system.

[9] The paper is organized as follows. The numerical model and details of the simulations are described in section 2, which compares the reconstructed fields to the original OGCM-simulated values and analyzes the reconstruction errors, the differences between the reconstructed and actual OGCM-simulated values. Section 3 presents an analysis of the reconstruction errors in the temperature, salinity, OUHC and MLD in the “standard” simulation (defined later). Section 4 analyzes sensitivity experiments designed to illuminate the effects of float movements (section 4.1) and the density of the spatial sampling coverage (section 4.2) on the expected accuracy of the Argo system. The analyses of section 4 are focused on a single variable, UOHC. The discussion and conclusions are presented in section 5.