Description: We present sea ice temperature and salinity data from first-year ice (FYI) and second-year ice (SYI) relevant to the temporal development of sea ice permeability and brine drainage efficiency from the early growth phase in October 2019 to the onset of spring warming in May 2020. Our dataset was collected in the central Arctic Ocean during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition in 2019 to 2020. MOSAiC was an international transpolar drift expedition in which the German icebreaker RV Polarstern anchored into an ice floe to gain new insights into Arctic climate over a full annual cycle. In October 2019, RV Polarstern moored to an ice floe in the Siberian sector of the Arctic at 85 degrees north and 137 degrees east to begin the drift towards the North Pole and the Fram Strait via the Transpolar Drift Stream. The data presented here were collected during the first three legs of the expedition, so all the coring activities took place on the same floe. The end dates of legs 1, 2, and 3 were 13 December, 24 February, and 4 June, respectively. The dataset contributed to a baseline study entitled, Deciphering the properties of different Arctic ice types during the growth phase of the MOSAiC floes: Implications for future studies. The study highlights downward directed gas pathways in FYI and SYI by inferring sea ice permeability and potential brine release from several time series of temperature and salinity measurements. The physical properties presented in this paper lay the foundation for subsequent analyses on actual gas contents measured in the ice cores, as well as air-ice and ice-ocean gas fluxes. Sea ice cores were collected with a Kovacs Mark II 9 cm diameter corer. To measure ice temperatures, about 4.5 cm deep holes were drilled into the core (intervals varied by site and leg) . The temperatures were measured by a digital thermometer within minutes after the cores were retrieved. The ice cores were placed into pre-labelled plastic sleeves sealed at the bottom end. The ice cores were transported to RV Polarstern and stored in a -20 degrees Celsius freezer. Each of the cores was sub-sampled, melted at room temperature, and processed for salinity within one or two days. The practical salinity was estimated by measuring the electrical conductivity and temperature of the melted samples using a WTW Cond 3151 salinometer equipped with a Tetra-Con 325 four-electrode conductivity cell. The practical salinity represents the the salinity estimated from the electrical conductivity of the solution. The dataset also contains derived variables, including sea ice density, brine volume fraction, and the Rayleigh number.

Description: First-year sea-ice thickness, draft, salinity, temperature, and density were measured during near-weekly surveys at the main first-year ice coring site (MCS-FYI) during the MOSAiC expedition (legs 1 to 4). The ice cores were extracted either with a 9-cm (Mark II) or 7.25-cm (Mark III) internal diameter ice corers (Kovacs Enterprise, US). This data set includes data from 23 coring site visits and were performed from 28 October 2019 to 29 July 2020 at coring locations within 130 m to each other in the MOSAiC Central Observatory. During each coring event, ice temperature was measured in situ from a separate temperature core, using Testo 720 thermometers in drill holes with a length of half-core-diameter at 5-cm vertical resolution. Ice bulk practical salinity was measured from melted core sections at 5-cm resolution using a YSI 30 conductivity meter. Ice density was measured using the hydrostatic weighing method (Pustogvar and Kulyakhtin, 2016) from a density core in the freezer laboratory onboard Polarstern at the temperature of –15°C. Relative volumes of brine and gas were estimated from ice salinity, temperature and density using Cox and Weeks (1983) for cold ice and Leppäranta and Manninen (1988) for ice warmer than –2°C. The data contains the event label (1), time (2), and global coordinates (3,4) of each coring measurement and sample IDs (13, 15). Each salinity core has its manually measured ice thickness (5), ice draft (6), core length (7), and mean snow height (22). Each core section has the total length of its top (8) and bottom (9) measured in situ, as well estimated depth of section top (10), bottom (11), and middle (12). The depth estimates assume that the total length of all core sections is equal to the measured ice thickness. Each core section has the value of its practical salinity (14), isotopic values (16, 17, 18) (Meyer et al., 2000), as well as sea ice temperature (19) and ice density (20) interpolated to the depth of salinity measurements. The global coordinates of coring sites were measured directly. When it was not possible, coordinates of the nearby temperature buoy 2019T66 were used. Ice mass balance buoy 2019T66 installation is described in doi:10.1594/PANGAEA.938134. Brine volume (21) fraction estimates are presented only for fraction values from 0 to 30%. Each core section also has comments (23) describing if the sample is from a false bottom, from rafted ice or has any other special characteristics. Macronutrients from the salinity core, and more isotope data will be published in a subsequent version of this data set.

Description: Author Posting. © American Geophysical Union, 2005. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 19 (2005): GB4006, doi:10.1029/2004GB002445.

Description: We report iron measurements for water column and aerosol samples collected in the Sargasso Sea during July-August 2003 (summer 2003) and April-May 2004 (spring 2004). Our data reveal a large seasonal change in the dissolved iron (dFe) concentration of surface waters in the Bermuda Atlantic Time-series Study region, from ∼1–2 nM in summer 2003, when aerosol iron concentrations were high (mean 10 nmol m−3), to ∼0.1–0.2 nM in spring 2004, when aerosol iron concentrations were low (mean 0.64 nmol m−3). During summer 2003, we observed an increase of ∼0.6 nM in surface water dFe concentrations over 13 days, presumably due to eolian iron input; an estimate of total iron deposition over this same period suggests an effective solubility of 3–30% for aerosol iron. Our summer 2003 water column profiles show potentially growth-limiting dFe concentrations (0.02–0.19 nM) coinciding with a deep chlorophyll maximum at 100–150 m depth, where phytoplankton biomass is typically dominated by Prochlorococcus during late summer.

Description: Funding for this work was provided by the U.S. National Science Foundation (OCE-0222053 to P. N. S., OCE-0222046 to T. M. C., and OCE-0241310 to D. J. M.), the U.S. National Aeronautics and Space Administration (NAG5-11265 to D. J. M.), the Australian Research Council (DP0342826 to A. R. B.), the Antarctic Climate and Ecosystems Cooperative Research Center, and the H. Unger Vetlesen Foundation.

Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Geophysical Research Letters 42 (2015): 8088–8097, doi:10.1002/2015GL065727.

Description: The Ross Sea sustains a rich ecosystem and is the most productive sector of the Southern Ocean. Most of this production occurs within a polynya during the November–February period, when the availability of dissolved iron (dFe) is thought to exert the major control on phytoplankton growth. Here we combine new data on the distribution of dFe, high-resolution model simulations of ice melt and regional circulation, and satellite-based estimates of primary production to quantify iron supply and demand over the Ross Sea continental shelf. Our analysis suggests that the largest sources of dFe to the euphotic zone are wintertime mixing and melting sea ice, with a lesser input from intrusions of Circumpolar Deep Water and a small amount from melting glacial ice. Together these sources are in approximate balance with the annual biological dFe demand inferred from satellite-based productivity algorithms, although both the supply and demand estimates have large uncertainties.