Radiative transfer (RT) approximations form the basis of semi-analytical (SA) inversion algorithms formulated to derive IOPs and subsequently biogeochemical parameters from ocean color remote sensing. Leading SA inversion algorithms, however, have not been rigorously assessed with respect to particulate volume scattering function (VSF) variability in the ocean, as, up until very recently, the comprehensive VSF data sets required for such an assessment have not been available. It is generally recognized by the community that there is likely no larger source of uncertainty in current SA algorithms to derive IOPs than the uncertainty associated with variability in VSF shapes, as the other important parameters in the inversions have been rigorously assessed (Loisel et al. 2001; Morel et al. 2002; Gordon 2002). Through field work since 2005 supported by NASA and other sources, we now have extensive in situ IOP data sets from 18 locations that contain fully resolved VSFs in a wide range of Case I and Case II waters throughout the world (Sullivan and Twardowski 2009; Czerski et al. 2011; You et al. 2011; Twardowski et al. 2012; Gilerson et al. 2013; Randolph et al., in press). Included in these data sets are NASA ocean color validation experiments with concurrently collected IOP (including VSF) and radiometric measurements, where the technological assets employed, scope of the measurements, and attention to accuracy make these data sets special and unique. Not only are they some of the few nominally complete data sets from an RT closure perspective that we are aware of (as they include full VSFs and full radiance distributions), but the data quality is the highest possible that can be achieved at this time. With such measurements in hand, we have an exciting opportunity to evaluate the effects of varying VSF shape on retrieval uncertainties for the leading SA inversions, and to assess performance for a variety of specific environmental conditions. There is also the opportunity to reevaluate RT approximations such as a Zaneveld (1995) model that explicitly includes VSF shape information, as these models have generally been avoided due to the historical lack of representative VSF data. The specific goals of this work are to 1) assess the full range of variability in VSF shapes in the ocean using an extensive data base of custom VSF measurements collected by our lab, 2) evaluate uncertainties in leading SA inversion approaches associated with this natural VSF variability through RT modeling, and 3) to resuscitate, rework, and evaluate uncertainties in native, analytical RT approximations developed by Zaneveld (1995) and Jerlov (1976) that have received little attention to date specifically because of the fact that they included explicit VSF formulations that could not be practically applied in the past. Our approach will first involve computing remote sensing reflectances from our extensive data sets using the Hydrolight RT solution. Reflectances will then be validated using concurrently measured radiometry data to assess convolved errors in the measurements (separate from uncertainties related to inversion approximations), and to ensure the data sets are sound from a theoretical standpoint. The SA inversion algorithms will then be applied, with the retrieved absorption and backscattering compared to the original measurements to quantify uncertainties. Our overall goal is to quantify and minimize errors in inversion results when applied to a representative range of observed VSFs in the ocean, leading to recommendations in SA algorithm applications for the future PACE mission.