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Fig. 5 in On Papulifères, putative ciliate cysts of diverse morphologies, with new observations from the plankton of the Chukchi Sea (Arctic Ocean)

Fig. 5. Locations of the sampling sites in the Chukchi Sea where Papulifère forms were found in plankton net tow material gathered during survey cruises in 2015, 2021, and 2022. The sites are numbered I to VII in chronological order of sampling. Details of the sites and sampling are given in Table 2.

Fig. 8 in On Papulifères, putative ciliate cysts of diverse morphologies, with new observations from the plankton of the Chukchi Sea (Arctic Ocean)

Fig. 8. Frequency distributions of the largest dimensions of the two forms found in the greatest abundance. The left panel shows the distribution of 53 specimens of the form shown in Fig. 6E, resembling Meunier's Fusopsis umbracula (Fig. 1B), parsed into size-classes of longest dimension. The right panel shows the distribution of 20 specimens of the form shown in Fig. 7B, resembling Meunier's Sphaeropsis brevisetosa (Fig. 1S), parsed into size-classes of longest dimension. The distributions of the size-classes appears more 'normal' than bi-modal' suggesting that single populations were sampled with wide size-ranges.

Fig. 4 in On Papulifères, putative ciliate cysts of diverse morphologies, with new observations from the plankton of the Chukchi Sea (Arctic Ocean)

Fig. 4. Illustrations of Papulifère forms said to be tintinnid cysts by Reid and John. From Reid and John 1978: A, B, J, K, & L. From Reid and John 1981: C, D, E, F, G, H, & I. Some were given specific designations: B: "cyst type P"; C: "cyst type S"; D: "cyst type T"; E: "cyst type M"; F: "cyst type F"; G: "cyst type Q"; H: "cyst type K; I: "cyst type N"; L: "cyst type O". Some of these specific designa- tions are still in use in the micropaleontology literature (e.g. Mudie et al. 2021a,b)

Fig. 7. The 14 in On Papulifères, putative ciliate cysts of diverse morphologies, with new observations from the plankton of the Chukchi Sea (Arctic Ocean)

Fig. 7. The 14 spherical/ovoid 'Sphaeropsis' papuliferid cyst forms found in Chukchi Sea plankton net tow material. All the specimens shown are from the 2022 sample station 16 (sample VII in Table 2), except the one shown in Fig. F. Scale bars all represent 50 µm.

Fig. 7 in Notes on the Occurrence of Tintinnid Ciliates, and the Nasselarian Radiolarian Amphimelissa setosa of the Marine Microzooplankton, in the Chukchi Sea (Arctic Ocean) Sampled each August from 2011 to 2020

Fig. 7. The growth in the list of tintinnid species reported from the Chukchi Sea beginning with the first sampling in 1953 up to and including our 2020 sampling. The list of the 60 species now known for Chukchi Sea, with sampling year first found for each species, and the reference, is given in the supplementary file. Data are from Bursa 1963, Matsuno et al. 2014, Li et al. 2016, Yokoi et al. 2016, Dolan et al. 2017, Xu et al. 2018, Wang et al. 2019, and this study.

Fig. 2. Sampling locations, 2011–2020 in Notes on the Occurrence of Tintinnid Ciliates, and the Nasselarian Radiolarian Amphimelissa setosa of the Marine Microzooplankton, in the Chukchi Sea (Arctic Ocean) Sampled each August from 2011 to 2020

Fig. 2. Sampling locations, 2011–2020 in Chukchi Sea. See supplementary file for details of station locations and sampling dates. Colored zones indicate water column depth.

Fig. 5 in Notes on the Occurrence of Tintinnid Ciliates, and the Nasselarian Radiolarian Amphimelissa setosa of the Marine Microzooplankton, in the Chukchi Sea (Arctic Ocean) Sampled each August from 2011 to 2020

Fig. 5. Morphological variability of Acanthostomella norvegica found in a single sample. The 18 cells shown were the first properly orientated cells encountered in sample aliquots from Station 17 of the 2017 cruise. Certain individuals shown (e.g., A and K) have lorica morphologies resembling those of a co-gener A. gracilis, first described as a variety of A. norvegica as they differ only in the near absence of an aboral horn. As shown here, the aboral point or horn appears to be quite variable.

Fig. 1 in Notes on the Occurrence of Tintinnid Ciliates, and the Nasselarian Radiolarian Amphimelissa setosa of the Marine Microzooplankton, in the Chukchi Sea (Arctic Ocean) Sampled each August from 2011 to 2020

Fig. 1. Examples of the distinct sea ice conditions in August encountered through the 10 cruises. Color-coding indicates areas of sea ice coverage varying from 100% to 0 %, or open water. The year 2012 was a record year of low sea ice extent while 2020 was year of sea ice extent more common in recent years.

Fig. 4 in Notes on the Occurrence of Tintinnid Ciliates, and the Nasselarian Radiolarian Amphimelissa setosa of the Marine Microzooplankton, in the Chukchi Sea (Arctic Ocean) Sampled each August from 2011 to 2020

Fig. 4. The distribution of lorica oral diameters (LOD) of the tintinnid species found over 10 years of sampling. The LOD of a species is related positively to the diameter of the preferred prey size, about 25% of the LOD. Note that the most common LODs are 19–30 µm and 35–42 µm, suggesting most species feed on small prey (5–10 µm diameter). Note also that excluding species found only once and species suspected to be morphological variants of another species (see Table 2) yields few changes in the distribution.

publicFeb 2025View details →

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The greater role of Southern Ocean warming compared to Arctic Ocean warming in shifting future tropical rainfall patterns

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Brighter Ocean: Arctic sea ice solar partitioning

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publicNov 2025View details →

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Future seasonal changes in habitat for Arctic whales during predicted ocean warming

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publicJan 2024View details →

openother-openOct 2016View details →

Methane and nitrous oxide data from the North American Arctic Ocean during summer, 2015

Methane, nitrous oxide, carbon isotope, and ancillary data used in "Methane and nitrous oxide distributions across the North American Arctic Ocean during summer, 2015"

opencc-by-4.0Apr 2024View details →

Data mining-based machine learning methods for improving hydrological data: a case study of salinity field in the Western Arctic Ocean

Salinity variations in Arctic Ocean determine the strength of stratification, ocean circulation, and biogeochemical cycles. Therefore, accurate salinity product is of great significance for our study of the Arctic Ocean. The mean density structure and wind-driven surface circulation of the Arctic Ocean are largely dominated by the anti-cyclonic Beaufort Gyre in the Canadian Basin, along with the Transpolar Drift (Hall et al.,2022). We focus on the salinity in Western Arctic Ocean. Multiple machine learning methods were used to reconstruct annual salinity product in the Western Arctic Ocean temporal for the period 2003-2022.

opencc-by-4.0Oct 2023View details →

Turbulent Fluxes For the Arctic Ocean 2003-2019

This turbulent flux data set is made with AIRS-only Version 7 data products, SSMR sea ice concentration and MERRA-2 10m winds.

opencc-by-4.0Nov 2023View details →

Metabarcoding dataset of surface microbial eukaryotes in the Western Arctic Ocean (Canada Basin/Beaufort Sea)

Fasta file of amplicon sequence variant (ASV) sequences, ASV phylogeny in Newick format, ASV table of relative abundance in samples and table of taxonomic information associated with each ASV. Raw sequence data were processed using DADA2 v1.14 and taxonomic assignment of ASV sequences was done using the PR2 database v4.12.0. The table of standardised environmental metadata (z-scores) is also provided.Additionally, the OTU (98%) table of relative abundances in samples and phylogeny in Newick format is available.

opencc-by-4.0Feb 2022View details →

Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning"

This repository contains additional model simulation data used in the following paper: Krasting et al., 2022: Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning. Communications Earth & Environment. Description of data files in this repository: <ol> <li>GFDL-CM4.c_ant.nc (42M) - NetCDF file of anthropogenic carbon inventory for 3 historical simulation ensemble members performed with the NOAA GFDL-CM4 climate model </li> <li>GFDL-ESM4.c_ant.nc (12M) - NetCDF file of anthropogenic carbon inventory for 3 concentration-driven historical simulation ensemble members performed with the NOAA GFDL-ESM4 Earth system model</li> <li>GFDL-ESM4e.c_ant.nc (12M) - NetCDF file of anthropogenic carbon inventory for 3 emission-driven historical simulation ensemble members performed with the NOAA GFDL-ESM4 Earth system model</li> </ol> Notes: <ul> <li>Anthropogenic carbon was calculated by vertically-integrating the dissolved inorganic carbon tracer (dissic) simulated at year 2002 and subtracting from the corresponding year of the preindustrial control simulation</li> <li>Results are provided on the models' native tripolar grids. Supporting grid metrics are provided in each NetCDF file</li> <li>All other model simulation data used in Krasting et al. 2022 is available publicly through the Earth System Grid Federation.</li> </ul>

opencc-by-4.0Apr 2022View details →

data for the paper "Role of Vertical Mixing in the Upper Ocean in the Seasonal Variation of Arctic Amplification" in 2nd round peer review

Here are all data for the paper "Role of Vertical Mixing in the Upper Ocean in the Seasonal Variation of Arctic Amplification", which is still during its peer review period.

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Tracking freshwater browning and coastal water darkening from boreal forests to the Arctic Ocean

The forest cover of Northern Europe has been steadily expanding during the last 120 years. More terrestrial vegetation and carbon fixation leads to more export to surface waters. This may cause freshwater browning, as more degraded plant-litter ends up as chromophoric (coloured) dissolved organic matter. Although most freshwater ultimately drains to coastal waters, the link between freshwater browning and coastal water darkening is poorly understood. Here, we explore this relationship through a combination of centennial records of forest and coastal water clarity, contemporary optical measurements in lakes and coastal waters, as well as an ocean drift model. We suggest a link between forest cover in Northern Europe and coastal water clarity in the Baltic, Kattegat and Skagerrak Sea and show how brown coloured freshwater from Northern European catchments can dictate coastal water clarity across thousands of kilometres, from the Baltic lakes to the Barents Sea.

opencc-zeroJul 2022View details →