In a simple example that performs SVD on a field of sea level pressure of the equatorial Pacific, svds outperforms irlba both in speed and correlation of singular vector (e.g. $u) to the output of the base svd function. One can see in the following graph, that trailing vectors break down in their correlation while svds maintains nearly perfect correlation. Interestingly, this artifact is removed by first centering the data field.
While the effect looks dramatic above, it should be noted that the trailing vectors usually carry only a small fraction of information, and thus contribute only marginally to errors in field reconstruction. Below is a figure showing the reconstruction error of svd, svds, and irlba with increasing levels of truncation.
Finally, both methods were compared in their performance within dineof. With the non-centered field both approaches arrive to a similar RMS, but svds converges with less iterations and EOFs than irlba. With the centered data both methods produce nearly identical results.
Code to reproduce: