DOI: 10.18739/a20p0wr0p
title: Hydrography Profiles, High Arctic, August-September 2018

description: This project was conducted as a part of a US-Swedish Joint Arctic Research Initiative. The goal of this initiative involved mooring the IB Oden to an ice floe in the inner pack ice in the high Arctic Ocean, and monitoring key oceanic-atmospheric parameters as the ice drifts. The cruise timeline (August through September) was chosen to highlight the transitional time period from the summer maximum in microbial biomass to declining stocks as autumn conditions result in lower nutrient and light levels, concomitant with the onset of freezing conditions. Biogenic aerosol production and fluxes are key research parameters in understanding the formation of cloud condensation nuclei (CCN) and their impacts on the radiation budget of the Arctic Ocean. At present, there exists a paucity of data regarding how microbial community composition might change in the high Arctic Ocean, especially with respect to changes in the production of volatile aerosol precursor compounds as pelagic microbial communities replace sympagic communities. Specifically, this project focused on linking microbial community structure with the oceanic-atmosphere fluxes of volatile organic carbon compounds (VOCs) emitted from various oceanic and pack ice ecosystems. The role of diminishing sea ice cover in the Arctic Ocean will significantly impact biogenic aerosol production and fluxes via changes in microbial community structure and the release of VOCs. At present, however, the scarcity of in-situ oceanic VOC measurements available from the high Arctic Ocean prevents the development of robust models correlating phytoplankton biomass with VOCs and their impact on aerosol production. For instance, most current models utilize satellite chlorophyll a (Chla) imagery for estimating phytoplankton biomass (e.g. Gabric et al. 2014; Becagli et al. 2016). It is also well recognized that high concentrations of sea surface chromophoric dissolved organic matter (CDOM) can significantly bias remotely-sensed Chla concentrations, especially when Chla levels are &amp;lt;0.5 mg m-3 (Matsuoka et al. 2017). In addition to the bias in estimating in-situ phytoplankton biomass from satellite-derived Chla, the contribution made by oceanic VOC fluxes to the atmospheric aerosol optical depth (e.g. Gabric et al., 2002) is unknown. Moreover, incorrect estimates of the oceanic mixed layer depth (MLD) using climatological datasets and/or subsurface Chla maximum can further compound the errors associated with attempting to correlate phytoplankton integrated water column production with estimates of biomass derived using satellite Chla algorithms (Arrigo et al., 2011). As a result, questions remain regarding the reliability of using Chla estimates as a surrogate to estimate the organic carbon enrichment in submicron marine aerosols (Rinaldi et al. 2013). Hence, models that use satellites over relatively large areal expanses in the Arctic may be biased with regards to estimates of biomass, net primary production and as a result correlations to biogenic aerosol (Arrigo et al., 2011, Becagli et al., 2016). More importantly, however, total Chla biomass is not the only important variable affecting the production of oceanic biogenic VOCs and aerosols. The microbial community composition and physiology will not only affect the cell-specific production rate of precursor biogenic aerosol compounds, but also the secondary transformations of those compounds. Furthermore, determining VOCs or phytoplankton functional groups from space are both fraught with even more difficulty than Chla estimates alone. Consequently, at present, virtually no data exists regarding the suite of VOCs released to the high Arctic atmosphere as a function of the in-situ microbial community composition. This dataset includes processed CTD data from 19 casts from August 2 to September 20, 2018. These data include temperature, salinity and density profiles, often up to 1000m, which provide information on water column structure and MLDs. Because most of these samples were collected in the same location, but over time, we begin to gain insight into changes in the water column, influenced by under-ice currents, ice coverage and wind intensities.