## 3 Results and Discussion  
### Database Characteristics  
GOCAD has over 40 times more CDOM records than NOMAD, and nearly 100 times as many spectra as IOCCG. It contains \(a_{g}\)(412) data that is more normally distributed than either NOMAD or IOCCG, and a Chl distribution similar to NOMAD (Figure 1). IOCCG model data, while covering the same dynamic range as the two field databases, have unrealistically flat distributions of both CDOM and Chl, raising concerns for introducing bias when the dataset is used for algorithm development and optimization. The dynamic range in \(a_{g}\)(\(\lambda\)) is larger in GOCAD than NOMAD (e.g., \(a_{g}\)(412) from 0.005 to 2.457 m\({}^{-1}\), and from 0.0013 to 1.923m\({}^{-1}\), respectively), but the data distribution of GOCAD is narrower than NOMAD and IOCCG (\(a_{g}\)(412) 75 th minus 25 th percentiles of 0.095 m\({}^{-1}\), 0.204 m\({}^{-1}\), 0.627 m\({}^{-1}\), respectively) around a lower mean absorption level (mean \(a_{g}\)(412) = 0.120 \(\pm\) 0.133 m\({}^{-1}\), 0.194 \(\pm\) 0.266 m\({}^{-1}\), 0.514 \(\pm\) 0.745 m\({}^{-1}\), respectively), reflecting the predominance of low CDOM, offshore data in the database. The large number of field records of CDOM in GOCAD, its range and mean value, all indicate that it is suitable for developing global retrieval algorithms. The data distributions shown along the bottom row of Figure 1 (with the exception of Chl--see figure caption) show data used to optimize algorithms developed in this study (i.e., from field stations with matching in situ radiometry and IOPs), versus data used for validation--in this case with SeaWiFS wave bands and satellite imagery. For each parameter, distributions of optimization and validation data were compared for similarity to test by analysis of variance (ANOVA) whether the populations share a common mean. Optimization and validation dataset were found to differ (\(p<<0.01\)) for CDOM absorption and spectral slope, but not for DOC and salinity. The difference between the CDOM and DOC match-up datasets results from availability of the data (i.e., stations may not have both CDOM and DOC measurements in addition to in situ radiometry). Based on the distributions shown in Figure 1, as well as the geographic distributions highlighted in Figure 3, differences between the optimization and validation data populations for CDOM absorption and slope appear to derive from slightly fewer near-shore stations being present in the optimization dataset compared to the validation set, although there is clearly some endmember representation in the optimization set for near-shore conditions. We may conclude from this, however, that algorithms for CDOM absorption and spectral slope developed using these optimization data would perform best in oceanic conditions, while regional algorithms may be more accurate in coastal waters, or waters with very high CDOM absorption and low CDOM spectral slope. IOCCG and NOMAD contain no UV CDOM data, so direct comparison of spectral slope is only possible in the VIS (Figure 2). The median \(S_{g}\)(412-600) is lower for GOCAD, demonstrating again the predominantly oceanic characteristics (i.e., photodegraded, primarily of marine origin, and presumably refractory) of the CDOM in the database. Slope decreases significantly (\(p<<0.01\)) as the reference wavelength (i.e., the shortest wavelength in the spectral range) increases from 275-412 nm. Overall, the variability in spectral slope for each range is quite low--generally no more than a factor of 2-3. This narrow dynamic range in slope within each waveband presents a challenge for retrieving fine scale differences in CDOM slope by limiting the sensitivity of algorithms built from inherently uncertain ocean color. However, errors in the retrievals should be small relative to the absolute magnitude of the slope even if the algorithm sensitivity (e.g., correlation between retrievals and field measurements) is low.