## 2 Methods: Modelling Approach
### Case Study Methods  
The complex optical interactions of \(D_{eff}\) and biomass, and the question of whether they can be separated into a useable PFT signal from a background environment of further non-algal optical complexity, is best addressed by investigating specific ecological events of interest to the remote sensing community. The case studies outlined in the _Introduction_ consider phytoplankton from two groups--a Chl \(a\)-carotenoid phytoplankton group, representing phytoplankton dominated by Chl \(a\), and fucoxanthin and/or peridinin; and a Chl \(a\)- and phycoerythrin-containing group [27]. The former group is chosen as representative of a wide range of phytoplankton across size classes, and the latter for the unique absorption characteristics of phycoerythrin-associated phytoplankton species. This selection is intentionally kept limited in order to assess the relative magnitude of particle size- vs. accessory pigment-related optical signals in likely ecological scenarios, in the context of changing biomass. Refractive indices for the chloroplast spheres are derived from measurements of cells from blooms in the Benguela--dimoflagellate and diatoms, dominated by Chl \(a\) and the carotenoid pigments fucoxanthin and peridinin--as well as for a phycoerythrin-associated cryptophyte group (based on a _Mesodinium rubrum_/_Myrioncta rubra_--dominated assemblage [27]). A justification for using these derived refractive indices across wide size ranges of modelled phytoplankton assemblages is included in Appendix A.1. Phytoplankton assemblages are modelled using a Standard Normal size distribution with a nominal effective variance of 0.6, recognising that while Jungian (exponential) distributionsare frequently used for bulk particulate in oceanic conditions, the former is more appropriate for representing the increased species monospecificity associated with elevated biomass ([34]), and the case studies refer mainly to biomass \(>1\) mg\(\cdot\)m\({}^{3}\). Assemblages are modelled with appropriate effective diameters to represent the effective diameters of measured size distributions in the case studies. The resulting IOPs are presented, with explanatory notes, in Appendix A.2. (A cyanobacterial group with substantially altered geometry to represent vacuolated cells has also been developed [63]). Phytoplankton IOPs are combined, in various proportions as indicated, with appropriate non-algal optical constituents as detailed for each experiment. The phytoplankton-related optical signal is assessed against variability in the non-algal contributions (detailed in Appendices A.3 and A.4), so their absolute magnitude is not critical. Both \(a_{gd}(\lambda)\) and \(b_{bmap}(\lambda)\) do, however, assume a smooth spectral shape with predictable spectral structure. The potential for additional spectral features in these contributions is not addressed here, and would add further complexity (and hence ambiguity) to resolving phytoplankton scattering characteristics. Water types are considered homogenous with depth (i.e., IOPs constant with depth), generic atmospheric and geographic conditions, and the full radiative transfer solution is calculated by Hydrolight at a spectral resolution of 5 nm. Given the technical challenges with using EAP phase functions for modelling high resolution spectra [35], a Fournier Forand phase function chosen for the backscatter fraction of the combined particulate IOPs is used at each wavelength throughout these experiments. A basic fluorescence efficiency model is included for completeness (detailed in Appendix B.2), but modelling this spectral region accurately is challenging and outside of the scope of this work, so the features of this spectral region are not discussed in terms of PFT sensitivity. 3.1 Southern Ocean Case Study: Separating the Effects of Biomass From the Effects of \(D_{eff}\) Change  
As shown in [PERSON] et al. [65] and [PERSON] et al.