## 1. Introduction

[2] Oceanic fluxes are usually examined in the framework of a decomposition in which a record *y*(*t*) is divided into mean and eddy components, *y*(*t*) = 〈*y*〉 + *y*′(*t*). Ergodicity must be assumed to replace the expectation operator 〈〉 with an average, e.g., the mean of all observations within a spatial bin. However, sparse, autocorrelated observations and the broad-band energetic nature of the eddy component, dominated by mesoscale and seasonal fluctuations, make reliable decomposition estimates from ocean data a daunting task.

[3] Satellite-tracked drifters [*Sybrandy and Niiler*, 1992] provide direct measurements of currents and SST throughout much of the world's oceans, offering a unique tool to examine processes such as mean and eddy transport of momentum and heat. Drifters are often treated as moving current meters. Lagrangian displacements are averaged in bins to produce pseudo-Eulerian maps of mean currents, and removal of this mean yields estimates of the eddy fluctuations. A similar procedure can be applied to drifter-observed SST. The primary strength of this binning technique is its simplicity–it requires no explicit assumptions for the structure of the mean or residual fields, although implicit assumptions are made when choosing bin size. More sophisticated methods [*Davis*, 1985; *Bauer et al.*, 1998] have been constructed to address spatial variations of the mean.

[4] All methods of mean/eddy decomposition require some assumptions regarding the residuals. For example, all assume that variations about the mean are stationary - in the simple binned method, this assumption is implicit when estimating the appropriate degrees of freedom for the quarter-day-interpolated observations within a bin. However, in addition to the mesoscale eddy field, the observations may contain a diagnosable fluctuation at a considerably longer period: the seasonal cycle. Can we neglect seasonal variations of currents and SST when performing a mean/eddy decomposition? Seasonal variations may bias a pseudo-Eulerian mean estimate when two necessary conditions are met: (1) observations sample one season more heavily than others, and (2) the seasonal cycle's amplitude is a significant fraction of the mean. We should answer “no” to the above question in regions where these conditions apply. But where might such regions be located?

[5] *Condition 1:* In the Atlantic Ocean, *Fratantoni* [2001] showed that the overall ensemble of drifter observations is nearly homogeneously distributed through the seasons. However, this is not true in smaller regions, e.g., the 1° square bins in which mean and eddy amplitudes are often calculated. To demonstrate this, the quarter-day-interpolated positions of all drogued Atlantic drifter observations prior to 1 August 2002 were assigned a complex number, with unit amplitude and phase set by the yearday (0° for 1 January, 180° for 30 June, ctc.). These were averaged in 1° bins to produce maps of the potential seasonal observation bias (Figure 1). An amplitude near zero indicates nearly homogeneous sampling through the seasons; amplitudes near unity show where one season is sampled exclusively. Nearly two-thirds of the bins exceed an amplitude of 0.3; past this threshold, bootstrap subsampling of a simulated signal (equal mean and annual amplitude and known true mean) yields a significantly biased estimated mean.

[6] *Condition 2:* SST has a well-known annual cycle in the subtropics. Tropical currents are suspected to display strong annual and semiannual variations [*Stramma and Schott*, 1999]. Previous studies of tropical Lagrangian observations have addressed this by subsetting the data for a particular season, e.g., the four seasonal maps of tropical Pacific currents of *Bauer et al.* [2002]. However, this approach discards the time-mean information of *all* the observations in a region, thus eliminating degrees of freedom which could be used to simultaneously estimate the mean, seasonal signal and residuals. The density of observations in much of the tropical Pacific is relatively large, so the discarded degrees of freedom are not crippling. This is not true in the tropical Atlantic, where a “summer” map (for example) would contain many bins with too few observations to estimate the summer-mean values.