A dataset of nearly 400 measurements of the particle size distribution (PSD) compiled from the Pacific, Atlantic, and Arctic Oceans is used to examine variability in the magnitude and shape of the PSD, and to characterize the partitioning of particle number, cross-sectional area, and volume concentration among defined size intervals. The results indicate that the relative contributions of three size classes based upon the pico-, nano-, and microplankton size range exhibit substantial changes among measures of particle size and between oceanic environments. The single-slope power law model commonly employed to characterize the PSD in aquatic studies is demonstrated to have significant limitations in capturing the complexity of PSD shapes observed for natural particle assemblages, and in consequence poorly predicts the relative contributions of these different size intervals. We show that specific percentile diameters derived from the cumulative distributions of particle size are strongly correlated with the contributions of these three size classes, and that these non-parametric descriptors of the cumulative distribution provide superior performance for estimating their contributions while requiring no assumption of underlying PSD shape. A comparison of these predictive relationships with independent field measurements suggests that this approach is generally robust for particle assemblages representing a wide diversity of marine environments.

An extensive data set of measurements within the Chukchi and Beaufort Seas is used to characterize the optical properties of seawater associated with different phytoplankton communities. Hierarchical cluster analysis of diagnostic pigment concentrations partitioned stations into four distinct surface phytoplankton communities based on taxonomic composition and average cell size. Concurrent optical measurements of spectral absorption and backscattering coefficients and remote-sensing reflectance were used to characterize the magnitudes and spectral shapes of seawater optical properties associated with each phytoplankton assemblage. The results demonstrate measurable differences among communities in the average spectral shapes of the phytoplankton absorption coefficient. Similar or smaller differences were also observed in the spectral shapes of nonphytoplankton absorption coefficients and the particulate backscattering coefficient. Phytoplankton on average, however, contributed only 25% or less to the total absorption coefficient of seawater. Our analyses indicate that the interplay between the magnitudes and relative contributions of all optically significant constituents generally dampens any influence of varying phytoplankton absorption spectral shapes on the total absorption coefficient, yet there is still a marked discrimination observed in the spectral shape of the ratio of the total backscattering to total absorption coefficient and remote-sensing reflectance among the phytoplankton assemblages. These spectral variations arise mainly from differences in the bio-optical environment in which specific communities were found, as opposed to differences in the spectral shapes of phytoplankton optical properties per se. These results suggest potential approaches for the development of algorithms to assess phytoplankton community composition from measurements of seawater optical properties in western Arctic waters.

The capability to estimate the oceanic particulate organic carbon concentration (POC) from optical measurements is crucial for assessing the dynamics of this carbon reservoir and the capacity of the biological pump to sequester atmospheric carbon dioxide in the deep ocean. Optical approaches are routinely used to estimate oceanic POC from the spectral particulate backscattering coefficient bbp, either directly (e.g., with backscattering sensors on underwater platforms like BGC-Argo floats) or indirectly (e.g., with satellite remote sensing). However, the reliability of algorithms which relate POC to bbp is typically limited due to the complexity of interactions between light and natural assemblages of marine particles, which depend on variations in particle concentration, composition, and size distribution. This study expands on our previous work by analysis of an extended field dataset created with judicious data inclusion criteria with the aim to provide POC algorithms for multiple light wavelengths of measured bbp, which can be useful for applications with in situ optical sensors as well as above-water active or passive measurement systems. We describe an improved empirical multivariable approach to estimate POC from simultaneous measurements of bbp and chlorophyll-a concentration (Chla) to better account for the effects of variable particle composition on the relationship between POC and bbp. The multivariable regression models are formulated using a relatively large dataset of coincident measurements of POC, bbp, and Chla, including surface and subsurface data from the Atlantic, Pacific, Arctic, and Southern Oceans. We show that the multivariable algorithm provides reduced uncertainty of estimated POC across diverse marine environments when compared with a traditional univariate algorithm based on only bbp. We also propose an improved formulation of univariate algorithm based on bbp alone. Finally, we examine performance of several algorithms to estimate POC using our dataset as well as a dataset consisting of optical measurements from BGC-Argo floats and traditional POC measurements collected during a coincident research cruise in the Atlantic Ocean.

The size distribution of suspended particles influences several processes in aquatic ecosystems, including light propagation, trophic interactions, and biogeochemical cycling. The shape of the particle size distribution (PSD) is commonly modeled as a single-slope power law in oceanographic studies, which can be used to further estimate the relative contributions of different particle size classes to particle number, area, and volume concentration. We use a data set of 168 high size-resolution PSD measurements in Arctic oceanic waters to examine variability in the shape of the PSD over the particle diameter range 0.8 to 120 μm. An average value of -3.6 ± 0.33 was obtained for the slope of a power law fitted over this size range, consistent with other studies. Our analysis indicates, however, that this model has significant limitations in adequately parameterizing the complexity of the PSD, and thus performs poorly in predicting the relative contributions of different size intervals such as those based on picoplankton, nanoplankton, and microplankton size classes. Similarly, median particle size was also generally a poor indicator of these size class contributions. Our results suggest that alternative percentile diameters derived from the cumulative distribution functions of particle number, cross-sectional area, and volume concentration may provide better metrics to capture the overall shape of the PSD and to quantify the contributions of different particle size classes.

Abstract. Radiative transfer (RT) simulations have long been used to study the relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean, from which ocean apparent optical properties (AOPs) can be calculated. For example, inverse models used to estimate IOPs from ocean color radiometric measurements have been developed and validated using the results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range of near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development is the generation of a synthetic dataset of seawater IOPs that serves as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by the probability distributions of IOPs that are consistent with global distributions representative of vast areas of open-ocean pelagic environments and coastal regions, covering a broad range of optical water types. The generation of synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs, assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column (not considered in previous synthetically generated optical databases) and three simulation scenarios associated with the sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0, 30, and 60∘ assuming clear skies, standard atmosphere, and a wind speed of 5 m s−1. Thus, overall 29 880 RT simulations were performed. The output results of these simulations include radiance distributions, plane and scalar irradiances, and a whole set of AOPs, including remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines, where all optical variables are reported in the spectral range of 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving IOPs and AOPs. The database is available at the Dryad open-access repository of research data (https://doi.org/10.6076/D1630T, Loisel et al., 2023).

Glacial meltwater is an important environmental variable for ecosystem dynamics along the biologically productive Western Antarctic Peninsula (WAP) shelf. This region is experiencing rapid change, including increasing glacial meltwater discharge associated with the melting of land ice. To better understand the WAP environment and aid ecosystem forecasting, additional methods are needed for monitoring and quantifying glacial meltwater for this remote, sparsely sampled location. Prior studies showed that sea surface glacial meltwater (SSGM) has unique optical characteristics which may allow remote sensing detection via ocean color data. In this study, we develop a first-generation model for quantifying SSGM that can be applied to both spaceborne (MODIS-Aqua) and airborne (PRISM) ocean color platforms. In addition, the model was prepared and verified with one of the more comprehensive in-situ stable oxygen isotope datasets compiled for the WAP region. The SSGM model appears robust and provides accurate predictions of the fractional contribution of glacial meltwater to seawater when compared with in-situ data (r = 0.82, median absolute percent difference = 6.38%, median bias = −0.04), thus offering an additional novel method for quantifying and studying glacial meltwater in the WAP region.

As the Western Antarctic Peninsula (WAP) region responds to a warmer climate, the impacts of glacial meltwater on the Southern Ocean are expected to intensify. The Antarctic Peninsula fjord system offers an ideal system to understand meltwater's properties, providing an extreme in the meltwater's spatial gradient from the glacio-marine boundary to the WAP continental shelf. Glacial meltwater discharge in Arctic and Greenland fjords is typically characterized as relatively lower temperature, fresh and with high turbidity. During two cruises conducted in December 2015 and April 2016 in Andvord Bay, we found a water lens of low salinity and low temperature along the glacio-marine interface. Oxygen isotope ratios identified this water lens as a mixture of glacial ice and deep water in Gerlache Strait suggesting this is glacial meltwater. Conventional hydrographic measurements were combined with optical properties to effectively quantify its spatial extent. Fine suspended sediments associated with meltwater (nanoparticles of ~ 5nm) had a significant impact on the underwater light field and enabled the detection of meltwater characteristics and spatial distribution. In this study, we illustrate that glacial meltwater in Andvord Bay alters the inherent and apparent optical properties of the water column, and develop statistical models to predict the meltwater content from hydrographic and optical measurements. The predicted meltwater fraction is in good agreement with in-situ values. These models offer a potential for remote sensing and high-resolution detection of glacial meltwater in Antarctic waters. Furthermore, the possible influence of meltwater on phytoplankton abundance in the surface is highlighted; a significant correlation is found between meltwater fraction and chlorophyll concentration.

Seasonal patterns in pathogen transmission can influence the impact of disease on populations and the speed of spatial spread. Increases in host contact rates or births drive seasonal epidemics in some systems, but other factors may occasionally override these influences. White-nose syndrome, caused by the emerging fungal pathogen Pseudogymnoascus destructans, is spreading across North America and threatens several bat species with extinction. We examined patterns and drivers of seasonal transmission of P. destructans by measuring infection prevalence and pathogen loads in six bat species at 30 sites across the eastern United States. Bats became transiently infected in autumn, and transmission spiked in early winter when bats began hibernating. Nearly all bats in six species became infected by late winter when infection intensity peaked. In summer, despite high contact rates and a birth pulse, most bats cleared infections and prevalence dropped to zero. These data suggest the dominant driver of seasonal transmission dynamics was a change in host physiology, specifically hibernation. Our study is the first, to the best of our knowledge, to describe the seasonality of transmission in this emerging wildlife disease. The timing of infection and fungal growth resulted in maximal population impacts, but only moderate rates of spatial spread.

Light emerging from natural water bodies and measured by radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities for characterizing aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission; the NASA Surface Biology and Geology designated observable mission; and NASA Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP-AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries, and oceans. Specific in situ measurements include remote-sensing reflectance, irradiance reflectance, and coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering. The dataset can be downloaded from https://doi.org/10.1594/PANGAEA.902230 (Casey et al., 2019).