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344 results for “North Atlantic Ocean”
Lagrangian overturning in the eastern subpolar North Atlantic Ocean - ORCA025-GJM189 Particle Trajectory Dataset
<p>This dataset contains the output of Lagrangian particle tracking experiments using 5-day mean velocity and hydrographic fields from the ORCA025-GJM189 ocean sea-ice model hindcast configured during the Drakkar project in which numerical particles are initialised along the northward inflows across the Overturning in the Subpolar North Atlantic Program (OSNAP) East section. Particles are advected using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool using the regular step-wise stationary advection scheme and an adapted implementation of the vertical turbulent mixing parameterisation created by Paris et al. (2013) for the Connectivity Modelling System Lagrangian particle tracking tool. This vertical turbulent mixing scheme only acts on particles found within the surface mixed layer (as evaluated along particle trajectories) and randomly reshuffles them according to a maximum vertical velocity of 10 cm/s - characteristic of vertical convective plumes. Note, particles cannot be artificially subducted across the base of the mixed layer into the ocean interior using this scheme.</p> <p>Particles are initialised on the first-available day of each month (based on the centre of the model fields 5-day mean windows) between 1976 and 2008 (inclusive) before being advected within the Iceland and Irminger Basins until any one of three termination conditions are met: 1) particles return southward across OSNAP East, 2) particles flow northward across the Greenland-Scotland Ridge, or 3) particles reach the maximum advection time of 7-years. The 7-year maximum advection time ensures >99.1% of all initialised particles meet one of conditions 1) or 2), hence only 0.9% of all particles are terminated between OSNAP East and the Greenland-Scotland Ridge.</p> <p>The number of particles initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 2.5 mSv (mSv == 1E-3 Sv), enabling the calculation of robust Lagrangian statistics.</p> <p>Particle locations (referenced to the original ORCA025 model grid) and properties (potential temperature, salinity, potential density and local mixed layer depth) are stored in the output files on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points.</p> <p>In total, four Lagrangian experiments were conducted at the Department of Earth Sciences, University of Oxford. Please see README.md for a full description of all Lagrangian experiments and the accompanying output files.</p> <p><strong>For a complete description of the ORCA025-GJM189 hindcast configuration see:</strong> https://github.com/meom-configurations/ORCA025.L75-GJM189.</p> <p><strong>For a complete description of TRACMASS v7.1 see</strong>: https://github.com/TRACMASS/tracmass</p>
Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: test datasets.
<p>We provide here the datasets used for the test and assessment of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>). </p> <p>The test dataset presented here includes different sets of co-located temperature and salinity vertical profiles: </p> <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units)</li> </ul> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, <em>submitted</em>.</p> <p>Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p> <p>Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.</p> <p> </p>
Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)
<p>Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.</p>
Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication
<p>The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the <em>Journal of Geophysical Research: Atmospheres</em>. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets:</p> <ul> <li> <p>Radiosonde data from 2-3 February 2020 (Stephan et al., 2021)</p> </li> <li> <p>Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020,</p> </li> <li> <p>GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020.</p> </li> <li> <p>Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017).</p> </li> <li> <p>W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below.</p> </li> </ul> <p>The present dataset is structured as follows:</p> <ul> <li> <p>diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle (filenames with extended dates 20200202 and 20200203)</p> </li> <li> <p>diurnal_cycle: files containing the diurnal cycle of each variable used in the publication</p> </li> <li> <p>binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper.</p> </li> <li> <p>satellite_data: a folder containing all satellite data used in the publication</p> </li> </ul> <p>Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls:</p> <ul> <li> <p>cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022)</p> </li> <li> <p>ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>)</p> </li> <li> <p>hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components. </p> </li> </ul> <p><br> </p> <p>References;</p> <p>Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>.</p> <p>Schmit, T. et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a></p> <p>Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a></p> <p>Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a></p> <p>Chin, T. M. et al., (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a></p>
Fig. 5 in Setaphyes elenae sp. nov., a new species of mud dragon (Kinorhyncha: Allomalorhagida) from Skagerrak (north-eastern Atlantic Ocean)
Fig. 5. Boxplots showing the ranges of different body measurements of Setaphyes elenae sp. nov., S. dentatus (Reinhard, 1881) and S. flaveolatus (Zelinka, 1928). A. Total trunk length. B. Standard sternal width. C. Lateral terminal spines' length.
Fig. 4 in Setaphyes elenae sp. nov., a new species of mud dragon (Kinorhyncha: Allomalorhagida) from Skagerrak (north-eastern Atlantic Ocean)
Fig. 4. Scanning electron micrographs showing general overview and details of the cuticular trunk morphology of a non-type specimen of Setaphyes elenae sp. nov. A. Dorsal overview. B. Middorsal elevation of segment 4. C. Cuticular ornamentation of anterior margin of segment 1. D. Detail of primary and secondary pectinate fringes of segment 5. E. Middorsal to paradorsal view of segment 2. F. Laterodorsal seta of segment 5. G. Middorsal process of segment 1. H. Ventral view of segments 4–5. I. Dorsal view of segment 10. J. Subdorsal sensory spots of segment 8. Abbreviations: mde = middorsal elevation; mdp = middorsal process; pdse = paradorsal seta; ppf = primary pectinate fringe; s = segment; vmse = ventromedial seta. Numbers after abbreviations indicate corresponding segment; sensory spots are marked as dashed circles. Scale bars: A = 100 µm; B, D, F–G, J = 1 µm; C, E, H–I = 10 µm.
Fig. 3 in Setaphyes elenae sp. nov., a new species of mud dragon (Kinorhyncha: Allomalorhagida) from Skagerrak (north-eastern Atlantic Ocean)
Fig. 3 (opposite page). Light micrographs showing trunk overviews and details of cuticular trunk characters of ♀, holotype (NHMD 655358) (A–L, N) and ♂, paratype (NHMD 655361) (M) of Setaphyes elenae sp. nov. A. Dorsal overview. B. Dorsal view on right half of segment 1. C. Ventral view on left half of segment 1. D. Dorsal view on right half of segment 2. E. Ventral view on left half of segment 2. F. Dorsal view on right half of segment 3. G. Ventral view on left half of segment 3. H. Ventral overview. I. Dorsal view on right half of segment 4. J. Ventral view on left half of segment 4. K. Dorsal view on right half of segment 8. L. Ventral view on left half of segment 8. M. Dorsal view on right half of segment 9. N. Ventral view on left half of segment 9. Abbreviations: ldse = laterodorsal seta; lts = lateral terminal spine; lvse = lateroventral seta; mde = middorsal elevation; mdp = middorsal process; pdse = paradorsal seta; vlse = ventrolateral seta; vmse = ventromedial seta. Numbers after abreviations indicate correspong segment; sensory spots are marked as dashed circles, and glandular cell outlets as continuous circles. Scale bars: A, H = 100 µm; B–G, I–N = 20 µm.
Fig. 2 in Setaphyes elenae sp. nov., a new species of mud dragon (Kinorhyncha: Allomalorhagida) from Skagerrak (north-eastern Atlantic Ocean)
Fig. 2 (opposite page). Line art illustrations of adult Setaphyes elenae sp. nov. A. ♀, ventral overview. B. ♀, dorsal overview. C. ♂, segments 10–11, ventral view. D. ♂, segments 1–2, ventral view. Abbreviations: bsj = ball-and-socket joint; dpl = dorsal placid; gcoI = type I glandular cell outlet; ldcr = laterodorsal cuticular ridge; ldse = laterodorsal seta; ldss = laterodorsal sensory spot; lts = lateral terminal spine; lvse = lateroventral seta; mde = middorsal elevation; mdp = middorsal process; ms = muscular scar; pdse = paradorsal seta; pdss = paradorsal sensory spot; ppf = primary pectinate fringe; ps = penil spine; pvap = paraventral apodeme; sdss = subdorsal sensory spot; spf = secondary pectinate fringe; vlcr = ventrolateral cuticular ridge; vlse = ventrolateral seta; vlss = ventrolateral sensory spot; vmse = ventromedial seta; vmss = ventromedial sensory spot; vmt = ventromedial tube; vpl = ventral placid. Scale bar = 100 µm
North Atlantic Oscillation (NAO) climate index hidden in ocean generated secondary microseisms
<p>Datatsets associated with "North Atlantic Oscillation (NAO) climate index hidden in ocean generated secondary microseisms". The data include the daily seismic cross-correlograms for station pairs located on land and at the seafloor offshore Ireland, 3D models used for the numerical simulations and the associated synthetic seismic data.</p>
F I G U R E 6 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 6 Back-calculated mean body length (±95% confidence interval) of Salmo salar at the midpoint of the winter annulus, following the conclusion of the post-smolt growth period
F I G U R E 1 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 1 Hierarchical cluster analysis of intercirculus spacing for scales of Salmo salar. (a) The dendrogram for k = 20 using Euclidean distance and Ward linkage for z-scored and interpolated data. The five major sub-branches (A–E) and the 20 clusters are ordered sequentially from the left. (b) The standardized intercirculus spacing plots for the 20 clusters. Clusters are colour-coded and ordered as in (a). The LOESS fits for each cluster are shown as a black line and the number of fish per cluster (n) is also shown
F I G U R E 2 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 2 Tabulation of significant under- and over-representation of the 10 most frequent growth pattern categories (and "Others") for Salmo salar scales amongst the 20 dendrogram clusters. Proportions of growth pattern frequency were compared to the overall population proportion of scales for k = 20 with Ward linkage, and clustering of the z-scored and interpolated data. Light shading (−) indicates significant under-representation and dark shading (+) indicates over-representation. Sample sizes (n) for each growth pattern across the time-series are shown
F I G U R E 4 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 4 Time-series changes in ocean surface temperature and Salmo salar scale growth pattern. (a) Changes in monthly SST anomaly for the 250 and 500 km standard deviation spatially weighted kernels in the Norwegian Sea (April 1992 – March 2011). (b) Changes in frequency (proportion within years) of selected growth patterns. The three selected pattern groupings illustrate fish showing persistent Fast growth (F) throughout the post-smolt growth season, Slow growth followed by Fast growth (SF), and all patterns pooled that displayed one or more growth Checks. The growth pattern data for each capture year (b) are aligned with the SST anomaly in April of the previous year (a), coinciding with the commencement of annual smolt emigration
F I G U R E 5 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 5 Monthly correlations between the SST anomalies throughout the post-smolt Salmo salar growth period and annual frequency of the Fast (F) and All Check growth patterns. The salmon data were lagged by -1 year to match the annual post-smolt growth seasons to the SST anomalies. Significant correlations (P <0.05; following adjustment of d.f. to allow for autocorrelation) are shown by the filled circles
Figures 2 and 3 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
Figures 2 and 3 summarize the under/over-representation of the more common growth patterns and years, respectively, amongst the k = 20 clusters. The SF growth pattern was over-represented for four of the five clusters of sub-branch A, in association with general underrepresentation of patterns showing an initial Fast (F) sequence (Figure 2). Sub-branch B revealed an essentially inverse structure to sub-branch A, with sporadic over-representation of patterns commencing with a Fast sequence and under-representation of those with an initial Slow sequence. Sub-branches C and D were heterogeneous
F I G U R E 3 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 3 Tabulation of significant under- and overrepresentation of the 20 dendrogram clusters amongst years of capture of return adult Salmo salar. Details as for Figure 2
Figure 4b in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
Figure 4b shows that there was (a) a decrease in the frequency of fish showing consistently Fast growth throughout the post-smolt period, (b) an increase in the frequency of the SF growth pattern and (c) an increase in the frequency of growth patterns including one or more Check sequences. Furthermore, these time-series changes in circulus pattern were linked significantly to contemporaneous and anomalous warming of the Norwegian Sea (Figure 5). As shown in Figure 6, one proximate consequence of these changes is manifest in the backcalculated mean length of fish at the midpoint of the winter annulus, following the completion of the post-smolt growth season. This showed a marked and significant decrease across the final six capture years of the time series.
Evolution of ocean circulation in the North Atlantic Ocean during the Miocene: impact of the Greenland Ice Sheet and the Eastern Tethys Seaway
<p>This dataset contains atmosphere and ocean outputs (NetCDF files) from modeling experiments with realistic early Miocene paleogeography as well as sensitivity to Greenland Ice Sheet and Eastern Tethys Seaway. The set of simulation targets the evolution of the North Atlantic Deep Water during the Miocene. The simulations have been run using the IPSL-CM5A2 General Circulation Model (Sepulchre et al. 2020 - IPSL-CM5A2 – an Earth system model designed for multi-millennial climate simulations, GMD). It includes 3 ocean-atmosphere simulations. Data are monthly averages over the last 100 years of the simulations. </p>
Fig. 7 in A new species of Ampharete (Annelida: Ampharetidae) from the West Shetland shelf (NE Atlantic Ocean), with two updated keys to the species of the genus in North Atlantic waters
Fig. 7. Ampharete oculicirrata sp. nov., paratype MNCN 16.01/18482_spec. 5. A. Anterior end, ventral view. B. Buccal tentacle with pinnae. C. First four abdominal uncinigers, lateroventral view. D. Posterior end, from AU8 to pygidium, ventral view. Abbreviations: AU = abdominal unciniger; bl = buccal lip; br = branchia; bt = buccal tentacle; btp = buccal tentacle pinna; pal = paleae; plc = pygidial lateral cirrus; pp = pygidial papillae; SG = segment; vpo = ventral pharyngeal organ. Scale bars: A, C = 150 µm; B = 25 µm; D = 100 µm.
Fig. 6 in A new species of Ampharete (Annelida: Ampharetidae) from the West Shetland shelf (NE Atlantic Ocean), with two updated keys to the species of the genus in North Atlantic waters
Fig. 6. Ampharete oculicirrata sp. nov., paratype MNCN 16.01/18482_spec. 3. A. Abdominal unciniger 7, posterior view. B. Detail of abdominal uncini, frontal and lateral view. Scale bars: A =150 µm; B = 5 µm.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.