## 4 Summary
A representational suite of approaches, including empirical, semi-analytical, and machine-learning algorithms was evaluated against GOCAD field data for retrieving \(a_{g}\) in six wavebands between 275 and about 490 nm, \(S_{g}\) in eight wavebands in the UV and VIS, and DOC using a wide variety of metrics. Ultimately, the most versatile and best performing of those tested was a simple, empirical set of relationships based on multiple linear regression between four wavebands of remote sensing reflectance (440-555 nm), with the exception of DOC which also required sea surface salinity (e.g., from Aquarius) to act as a proxy to optical water type. Results varied, with CDOM retrievals showing regression coefficients to field data (r\({}^{2}\)) generally over 0.80 for field radiometry to within 16%-34%, depending on the wavelength, and within 33%-54% for MODIS Aqua validation. CDOM slopes retrievals were best in the UVB (e.g., r\({}^{2}\) = 0.62, \(MAPD\) = 11% in satellite validation of \(S_{g}\)(275-295)), while DOC algorithms only optimized well after the inclusion of salinity (r\({}^{2}\) = 0.91, \(MAPD\) = 15%), and did not perform well in validation (e.g., RMSE = 27-29 \(\upmu\)mol L\({}^{-1}\)). Our analysis of the sensitivity of the DOC algorithm performance to factors such as salinity, DOC, water column depth, and geographic location ultimately proved inconclusive, exposing only a small anomaly involving overestimates of DOC in the southern oceans using MODIS Aqua imagery. Further validation--particularly in mid-ocean gyres where DOC varies very weakly or not at all with CDOM absorption, and salinity changes are very small--is clearly warranted prior to application of the DOC algorithm in those regions. Application of CDOM algorithms to monthly and climatological Aqua imagery demonstrated that global retrievals of CDOM do not covary well with similar empirical retrievals of Chl, but rather appear to follow Chl on a seasonal lag depending on the region and source of dissolved material. This helps explain the lack of correlation between CDOM and Chl found in global GOCAD field data and described in previous studies, and further challenges the use of Case 1 assumptions in bio-optical remote sensing. Surface CDOM concentration varies in regions such the western equatorial Pacific by about 150% over the course of long-term climatological shifts associated with ENSO, fluctuating in tight correlation with the MEI and CDOM slope. Algorithms developed here may be applied to tracking ENSO behavior in the future, as well as observing changes in CDOM character and concentration associated with global warming. Conceptualization, D.A. and A.M.; Data Curation, D.A. ; Formal Analysis, D.A. and D.L. ; Funding Acquisition, A.M.; Investigation, D.A. ; Methodology, D.A. and A.M.; Project Administration, A.M. and D.A. ; Resources, A.M.; Software, D.A. and D.L. ; Validation, D.A. and D.L. ; Visualization, D.A. ; Writing--original draft, D.A. ; Writing--review & editing, D.A. and A.M. This research was funded by the NASA ROSES Science of Aqua and Terra--MODIS project. We would like to acknowledge all investigators who have contributed to the SeaBASS database and the various Hansell/Carlson datasets (see Table 2). Thanks also to [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] of the NASA Ocean Biology Processing Group for data acquisition and programming support. The authors declare no conflict of interest.