### Spectral Shape of CDOM  
The CDOM absorption coefficient is generally modeled with an exponentially decaying function with increasing wavelength, \(\lambda\). \[a_{g}(\lambda)=a_{g}(\lambda_{0})\;e^{-Sg\;(\lambda-\lambda_{0})} \tag{1}\]  
where \(S_{g}\) is the spectral slope parameter and \(\lambda_{0}\) is a reference wavelength. \(S_{g}\) in various spectral ranges in the UV and VIS contains information about CDOM's photoreactive state, chemical composition, molecular weight distribution, and origin [4; 7; 18; 19; 20; 21]. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Units** & **Definition** \\ \hline S\({}_{g}(\lambda_{1}\)-\(\lambda_{2})\) & nm\({}^{-1}\) & Exponential slope of CDOM and in select spectral range \\ SPM & mg m\({}^{-3}\) & Suspended Particulate Material \\ TOC & \(\mu\)mol L\({}^{-1}\) & Total Organic Carbon \\ AOP & & Apparent Optical Properties \\ GIOP & & Generalized IOP Algorithm \\ GOCAD & & Global Ocean Carbon Algorithm Database \\ HMW & & High Molecular Weight \\ IOCCG & & International Ocean-Colour Coordinating Group \\ IOP & & Inherent Optical Properties \\ LMW & Low Molecular Weight \\ MLR & Multiple Linear Regression Algorithm \\ MODIS & Moderate Resolution Imaging Spectroradiometer \\ NAP & & Non-Algal Particulate \\ NOMAD & NASA bio-Optical Algorithm Dataset \\ QAA & Quasi-Analytical Algorithm \\ RFTB & Random Forest Tree Bagger Algorithm \\ SAA & Semi-Analytical Algorithm \\ SeaBASS & SeaWiFS Bio-optical Archive and Storage System \\ SeaWiFS & Sea-viewing Wide Field-of-view Sensor \\ UV, UVA, UVB & Ultraviolet spectrum, 315–400 nm, 280–315 nm \\ VIS & Visible spectrum \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. While the single exponential model in Equation (1) is accurate within limited wavebands, CDOM spectral slope, \(S_{g}\), is not constant across the UV and VIS and depends on the wavelength range used, spectral resolution, and reference wavelength \(\lambda_{0}\). Furthermore, comparative analysis of CDOM spectral shape as reported in the literature has been confounded by the multitude of methodologies and reference wavebands used historically to calculate \(S_{g}\)[9]. For instance, a linear fit to logarithmically transformed \(a_{g}\) data yields results for \(S_{g}\) biased by higher wavelength absorption, whereas a least-squared difference minimization fitting favors the lower wavelengths where the magnitude of \(a_{g}\) is higher, and is considered more accurate [7; 22]. Changes to \(S_{g}\) resulting from photodegradation are wavelength dependent, i.e., increasing below 460 nm and decreasing above 510 nm [23], although when calculated across the VIS from 412-555, slope is expected to increase through the destruction of large humic complexes resulting in lower molecular weight CDOM [24]. This effect appears to reverse over time as more refractory, low-molecular weight compounds are also degraded, thereby reducing CDOM absorption at shorter wavelengths relative to longer, and decreasing spectral slope across the VIS. All these factors lead to challenges in comparing CDOM spectral slope between studies, and a more standardized approach to CDOM spectral shape measurement still seems warranted [19]. The concept of the spectral slope curve, \(S_{g}(\lambda)\)--analogous to the first derivative of \(S_{g}\) with respect to \(\lambda\)--was explored by [PERSON] et al. in 2009 [23]. Calculating \(S_{g}\) from natural waters, cultures, and laboratory standards at 20 nm waveband intervals between 200-700 nm, they showed that \(S_{g}(\lambda)\) had complex spectral characteristics including peaks near 390 nm likely indicating a prevalence of autochthonous production of fulvic acid-type CDOM, and near 280 nm possibly due to the release of proteins or phenols by phytoplankton. While the spectral slope curve approach of [PERSON] et al.