A document by Nathan J.M. Laxague. Click on the document to view its contents.

Solar heating of the upper ocean is a primary energy input to the ocean-atmosphere system, and the vertical heating profile is modified by the concentration of phytoplankton in the water, with consequences for sea surface temperature and upper ocean dynamics. Despite the development of increasingly complex modeling approaches for radiative transfer in the atmosphere and upper ocean, the simple parameterizations of radiant heating used in most ocean models are plagued by errors and inconsistencies. There remains a need for a parameterization that is reliable in the upper meters and contains an explicitly spectral dependence on the concentration of biogenic material, while maintaining the computational simplicity of the parameterizations currently in use. In this work, we assemble simple, observationally-validated physical modeling tools for the key controls on ocean radiant heating, and simplify them into a parameterization that fulfills this need. We then use observations from 64 spectroradiometer depth casts across 6 cruises, 13 surface hyperspectral radiometer deployments, and 2 UAV flights to probe the accuracy and uncertainty associated with the new parameterization. We conclude with a case study using the new parameterization to demonstrate the impact of chlorophyll concentration on the structure of diurnal warm layers, an investigation that was not possible to conduct accurately using previous parameterizations. The parameterization presented in this work equips researchers to better model global patterns of sea surface temperature, diurnal warming, and mixed-layer depths, without a prohibitive increase in complexity.

The community of Kotzebue, located on the coast of Kotzebue Sound, which is northeast of the Bering Straits adjacent to the Chukchi Sea, is reliant on the waters around Kotzebue Sound for food and economy. There have been reports of cyanobacterial blooms in these waters around Kotzebue but they have not been systematically studied yet, because the region is sparsely populated with few in-situ observations. Cyanobacteria often form surface blooms in freshwater and coastal ecosystems which can be detected using remote sensing techniques. Cyanobacteria are found to have low nutritional value and many species of cyanobacteria produce cyanotoxins, and thus can be harmful to aquatic life and cause public health hazards. In addition, consumption of decaying cyanobacterial blooms by microbes depletes oxygen level which can lead to hypoxia, adversely impacting the benthic community. As the Arctic is warming twice as fast as the rest of the planet due to climate change, thawing permafrost is releasing nutrients that might be enhancing cyanobacterial blooms in the coastal, marine and lacustrine waters of Alaska. In this study, we used remote sensing to study phytoplankton biomass, turbidity and cyanobacterial blooms between mid-June to end of September each year from 2013 to 2019 when the waters around Kotzebue are ice-free. Using images from Landsat-8 and Sentinel-2, processed using ACOLITE software, we investigated spatial and temporal changes in water quality parameters such as turbidity and chlorophyll concentration between June and September. We used a combination of true-color images and fai (floating algal index) to detect cyanobacterial blooms. There were about two scenes from Sentinel-2 and about one scene from Landsat-8, for a total of about three scenes every week between June and September. Of these, only 49% of the images were cloud-free. Of the cloud-free images, 29% were found to have a cyanobacterial bloom between August and September for an average of two to four scenes every year. Most of the cyanobacterial blooms were detected in Kobuk Lake near Kotzebue, and nearby sites in Hotham Inlet and Selawik Lake. In 2013, 68% of the images were cloudy which was the highest in the observed years and no cyanobacterial blooms were detected.

The Amazon River discharges more than 200,000 m3 s-1 into the Western Tropical Atlantic Ocean from May to June. The low salinity surface plume extends more than 1800 km from the mouth and covers an area greater than 1 million square kilometers. We hypothesize that the plume exhibits distinct microbial community assemblages driven by plume age, nutrient supply, and light availability. We collected samples for nutrients and flow-cytometry measurements to investigate the spatial variability of the cyanobacteria Prochlorococcus spp. and Synechococcus spp., picoeukaryotes, and heterotrophic bacteria. Overall the surface salinity of the water we sampled ranged from 15.5 ppt at the southernmost station to 36.3 ppt in the open ocean station. The surface nitrate and soluble reactive phosphorus concentrations ranged from below detection limit to 3.3 µM and 2 µM, respectively. Generally, in the freshest surface plume waters (15-28 ppt) we found the highest abundances of Synechococcus spp., picoeukaryotes, and hetrotrophic bacteria with little or no Prochlorococcus spp. In the transition of surface salinities from 28 ppt to 32 ppt, a population of Prochlorococcus spp. began to form below the surface plume while Synechococcus spp. abundances at the surface remained unchanged and picoeukaryotes, and heterotrophic bacteria abundances decreased. As the surface salinity climbed over 32 ppt, the Prochlorococcus spp. abundance was uniformly high throughout the euphotic zone. On the other hand, as surface salinities increased over 32 ppt Synechococcus spp. abundances at the surface gradually decreased, while picoeukaryote and hetrotrophic bacterial abundances remained constant. We will discuss changes in the microbial community composition as a function of nutrient and light availability, as well as plume age in the Amazon Plume-Ocean continuum in both surface and deep chlorophyll maximum assemblages.

Waterways such as the Hudson River play an integral role in agriculture, health, transportation, recreation, energy, and sustaining biodiversity. Although water pollution in New York Harbor has been extensively studied, the reduction of millions of commuters during the COVID-19 lockdown presents an unprecedented opportunity to study human impact on water quality. We used remote sensing data to assess how the COVID-19 lockdown impacted water quality in New York Harbor, particularly in areas near Combined Sewer Outfalls (CSOs). This technique has previously been used to measure water quality in the Hudson River. We used ACOLITE to process Landsat-8 and Sentinel-2 images from 2015-2020. The algorithms “t_nechad”, “spm_nechad”, and “kdpar_qaasw” were used to measure turbidity, and “chl_oc2”, “chl_oc3”, “chl_re_moses3b”, “chl_re_moses740”, and “chl_re_mishra” to measure chlorophyll concentration. After uploading processed images into SeaDAS, we extracted values from pixels corresponding to Department of Environmental Protection (DEP) field sites. At these sites, the DEP measures Total Suspended Solids and Chlorophyll A Concentration using optical turbidity sensors and fluorometers, respectively. By comparing pixel values with DEP data we determined that the chlorophyll algorithms did not produce accurate readings of chlorophyll concentration in New York Harbor. We focused on analyzing turbidity at five DEP sites, four of which were located around wastewater treatment plants, to assess any CSO-induced changes in water quality. The frequency of usable satellite data from 2020 was severely limited by cloudiness, so we combined Landsat-8 and Sentinel-2 turbidity measurements (R = 0.8685) to form time series for each site. We expected to see a decrease in turbidity during the lockdown period, due to a decrease in sewage from office buildings. However, turbidity strongly fluctuated throughout all years with no discernable temporal pattern, and we could not distinguish between 2020 measurements and seasonal patterns. Thus, preliminary analysis shows that there was no significant variation in water turbidity due to the COVID-19 lockdown.

Most of Earth’s volcanic eruptions occur underwater, and these submarine eruptions can significantly impact large-scale earth systems. In this study, we develop a new semi-automated analysis framework to detect submarine eruptions through the supervised classification of satellite images on Google Earth Engine (GEE). We present a case study from the Rabaul caldera region in Papua New Guinea and find a large number of new unreported pumice rafts (in ~16% of images from 2017–present). After analysis of the spatial pattern of raft sightings and ancillary observations, we interpret that these rafts are not the result of a new eruption. Instead, we posit that the observed rafts represent remobilization of pumice clasts from previous historical eruptions. This novel process of raft remobilization may be common at near-shore/partially submarine caldera systems (e.g., Rabaul, Krakatau) and has significant implications for new submarine eruption detection, volcanic stratigraphy, and biological dispersal by rafts.