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7 results for “under-ice”

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edi56/100

High Frequency Under-Ice Water Temperature Buoy Data - Crystal Bog, Trout Bog, and Lake Mendota, Wisconsin, USA 2016-2020

Water temperature measurements from three Wisonsin lakes. Two bog lakes are in Northern Wisconsin, Lake Mendota is in Southern Wisconsin. Thermistor chains span the full depth of each lake. See freeze dates for periods of open or frozen lake (NTL 32, DOI 10.6073/pasta/1c1acdb5489a0355f6f8bb5c496fdf8b and NTL 33, DOI 10.6073/pasta/22a5b5f8bce193353e559918b0024f9d)

openCC (other)Dec 2022View details →
zenodo52/100

Dataset: Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean

<p>This dataset is linked to this manuscript entitled &quot;Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean&quot; published in Elementa: Science of the Anthropocene (<a href="http://doi.org/10.1525/elementa.430">http://doi.org/10.1525/elementa.430</a>). Please find the abstract below:</p> <p>The decline of sea-ice thickness, area, and volume due to the transition from multi-year to first-year sea ice improves the under-ice light environment for pelagic Arctic ecosystems. One unexpected and direct consequence of this transition,&nbsp;the proliferation of under-ice phytoplankton blooms (UIBs),&nbsp;challenges the paradigm that waters beneath the ice pack harbor little planktonic life. Little is known about the diversity and spatial distribution of UIBs in the Arctic Ocean, or the environmental&nbsp;drivers behind their timing, magnitude, and species composition. Here, we compiled a unique and comprehensive dataset from seven major research projects in the Arctic Ocean (11 expeditions, covering the spring sea-ice-covered period to summer ice-free conditions) to identify the environmental drivers responsible for initiating and shaping the magnitude and assemblage structure of UIBs.&nbsp;The temporal dynamics behind UIB formation related to the ways that snow and sea-ice conditions impact the under-ice light field. In particular, the&nbsp;onset of snowmelt&nbsp;significantly increased under-ice light availability (&gt; 0.1&ndash;0.2 mol photons m<sup>&ndash;2</sup>&nbsp;d<sup>&ndash;1</sup>), marking the concomitant termination of the sea-ice algal bloom and initiation of UIBs. At the pan-Arctic scale, bloom magnitude (expressed as maximum chlorophyll&nbsp;<em>a&nbsp;</em>concentration) was predicted best by winter water Si(OH)<sub>4</sub>&nbsp;and PO<sub>4</sub><sup>3&ndash;</sup>&nbsp;concentrations, as well as Si(OH)<sub>4</sub>:NO<sub>3</sub><sup>&ndash;</sup>&nbsp;and PO<sub>4</sub><sup>3&ndash;</sup>:NO<sub>3</sub><sup>&ndash;</sup><sub>&nbsp;</sub>drawdown ratios, but not NO<sub>3</sub><sup>&ndash;</sup>&nbsp;concentration. Two main phytoplankton assemblages dominated UIBs (diatoms or&nbsp;<em>Phaeocystis</em>), driven primarily by the winter nitrate:silicate (NO<sub>3</sub><sup>&ndash;</sup>:Si(OH)<sub>4</sub>) ratio and the under-ice light climate.&nbsp;<em>Phaeocystis</em>&nbsp;co-dominated in low Si(OH)<sub>4</sub>&nbsp;(i.e., NO<sub>3</sub>:Si(OH)<sub>4</sub>&nbsp;molar ratios &gt; 1)&nbsp;waters, while diatoms contributed the bulk of UIB biomass when Si(OH)<sub>4</sub>&nbsp;was high (i.e.,&nbsp;NO<sub>3</sub>:Si(OH)<sub>4</sub>&nbsp;molar ratios &lt; 1). The implications of such differences in UIB composition could have important ramifications for Arctic biogeochemical cycles, and ultimately impact carbon flow to higher trophic levels and the deep ocean.</p>

opencc-by-4.0Jul 2020View details →
edi48/100

Lake Fryxell under-ice conductivity, temperature, and depth (CTD) profiles, McMurdo Dry Valleys, Antarctica, January 2025

To characterize the upper ~2 m of the water column beneath the ~3 m ice cover in Lake Fryxell, located in the McMurdo Dry Valleys of Antarctica, 27 CTD (conductivity, temperature, depth) profiles were collected at ~5 m horizontal spacing along a transect spanning the dive hole to the shoreline. Measurements were made using a YSI Castaway CTD mounted on a 1 m mast atop a remotely operated vehicle (ROV), profiling upward from ~2 m below the ice-water interface. Using an ROV rather than drilling a hole through the ice was intended to minimize artifacts associated with ice openings. Sampling late in the austral summer targeted conditions during maximum seasonal ice melt.

openCC (other)Dec 2025View details →
edi48/100

Greenhouse gas and water chemistry data from urban ponds in Madison, Wisconsin during the summer and under-ice period of 2021-2022

Stormwater ponds are common features in urbanized landscapes and can suffer from rapid oxygen depletion when thermally stratified or ice-covered. To investigate under-ice oxygen dynamics and drivers of bottom water oxygen saturation, we sampled 20 stormwater ponds in Madison, Wisconsin, USA during the summer of 2021 and winter 2022. The urban ponds ranged in age, shape, size, and depth. We repeatedly took YSI profiles of water temperature, oxygen, and specific conductance 7 times in the summer and 3 times in the winter. Water chemistry variables were collected in the surface waters, habitat surveys were conducted in the summer, and ice/snow thickness was recorded in the winter. We also measured the concentration of greenhouse gases in the surface waters as a consequence to oxygen depletion using the headspace equilibrium method.

openCC (other)May 2024View details →
edi44/100

Under-ice lake physicochemical data, Missisquoi Bay and Shelburne Pond, Vermont, USA, 2014-2015

This dataset contains under-ice physicochemical data from Missisquoi Bay and Shelburne Pond in Vermont, USA collected during the 2013-2014 and 2014-2015 winters and spring thaw periods. The main dataset contains data for ice depths, water column sampling depths, nutrient chemistry, chlorophyll a, and relevant physicochemical variables. Ancillary data includes nearby air temperature data for the study periods as well as river discharge data for contributing or nearby tributaries. Data collection is complete.

openCC (other)Nov 2021View details →
edi36/100

Ice phenology and under-ice temperature and oxygen data for lakes of the Sierra Nevada, CA

<anchor id="_Hlk62995535"/>Warming winters will reduce ice cover and change under-ice conditions in temperate mountain lakes, where snow comprises most of winter cover on lakes. Snow-dominated mountain lakes are abundant and highly susceptible to climate warming, yet we lack an understanding of how climate variation and local attributes influence winter processes. We investigated climatic and intrinsic controls on ice phenology, water temperature, and bottom-water dissolved oxygen (DO) in 15 morphologically diverse lakes in the Sierra Nevada and Klamath mountains of California, USA, using high frequency measurements from multiple (2-5) winters. We found that ice phenology was determined by winter climate variables (snowfall, air temperature) that influence ice-off timing, whereas ice-on timing was relatively invariant among years. Lake size and morphology mediated the effect of climate on lake temperature and DO dynamics in early and late winter. Rates of hypolimnetic DO decline were highest in small, shallow lakes, and were unrelated to water temperature. Temperature and oxygen dynamics were more variable in small lakes because heavy snowfall caused ice submergence, mixing, and DO replenishment that affected the entire water column. As persistence of snow declines in temperate mountain regions, autumn and spring climatic conditions are expected to gain importance in regulating lake ice phenology. Water temperature and DO will likely increase in most lakes during winter as snowpack declines, but morphological attributes such as lake size will determine the sensitivity of ice phenology and under-ice processes to climate change.

openCC (other)Jul 2021View details →
zenodo32/100

Observational data of temperature and oxygen for the study "High-frequency observations of temperature and dissolved oxygen reveal under-ice convection in a large lake"

<p>Matlab data for both temperature and oxygen time series between December 1, 2014, 0:00 (EST) and April 26, 2015, 0:00 (EST). Temperature profiles are sampled every 20 seconds. Dissolved oxygen are sampled every 30 minutes. The depth of each time series are written on the file name.</p>

opencc-by-4.0Oct 2017View details →

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