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234 results for “Global Ocean”
Data from: Combining mesocosms with models to unravel the effects of global warming and ocean acidification on a temperate marine ecosystem
<p><span>Ocean warming and species exploitation have already caused large-scale reorganization of biological communities across the world. Accurate projections of future biodiversity change require a comprehensive understanding of how entire communities respond to global change. We combined a time-dynamic integrated food web modelling approach (Ecosim) with previous data from community-level mesocosm experiments to determine the independent and combined effects of ocean warming and acidification, and fisheries exploitation, on a well-managed temperate coastal ecosystem. The mesocosm parameters enabled important physiological and behavioural responses to climate stressors to be projected for trophic levels ranging from primary producers to top predators, including sharks. Through model simulations, we show that under sustainable rates of exploitation, near-future warming or ocean acidification in isolation could benefit species biomass at higher trophic levels (e.g., mammals, birds, and demersal finfish) in their current climate ranges, with the exception of small pelagic fish. However, under warming and acidification combined biomass-increases at higher trophic levels will be lower or absent, whilst in the longer term reduced productivity of prey species is unlikely to support the increased biomass at the top of the food web. We also show that increases in exploitation will suppress any positive effects of human-driven climate change, causing individual species biomass to decrease at higher trophic levels. Nevertheless, total future potential biomass of some fisheries species in temperate areas might remain high, particularly under acidification, because unharvested opportunistic species will likely benefit from decreased competition and show an increase in biomass. Ecological indicators of species composition such as the Shannon diversity index declined under all climate change scenarios, suggesting a trade-off between biomass gain and functional diversity. By coupling parameters from multi-level mesocosm food web experiments with dynamic food web models, we were able to simulate the generative mechanisms that drive complex responses of temperate marine ecosystems to global change. This approach, which blends theory with experimental data, provides new prospects for forecasting climate-driven biodiversity change and its effects on ecosystem processes.</span></p>
Global oceanic seamless POC concentration products derived from MODIS-Aqua
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2011 to 2016, derived from MODIS-Aqua‘s XGBoost satellite retrieval products. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.</p>
Global oceanic seamless POC concentration products derived from MODIS-Aqua
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2003 to 2010, derived from MODIS-Aqua‘s XGBoost satellite retrieval products. It covers the time span from 2003 to 2010. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.</p>
Global oceanic seamless POC concentration products derived from MODIS-Terra
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2001 to 2008, derived from MODIS-Terra‘s XGBoost satellite retrieval products. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.</p>
CESM2 Mechanically Decoupled (MD) for "Summer westerly wind intensification weakens Southern Ocean seasonal cycle under global warming" - submitted to Geophysical Research Letters
<p>CESM2 Experiment names:</p> <ul> <li>MD = mechanically decoupled model (referred to as MD in paper)</li> <li>FC = fully coupled model (referred to as FC in paper)</li> </ul> <p>Decoding file names:</p> <ul> <li>ensmean refers to ensemble mean</li> <li>trend refers to linear trend over 1950-2014</li> </ul> <p>Variables:</p> <ul> <li>SST = sea surface temperature</li> <li>HMXL = mixed layer depth</li> <li>WSPDSRFAV = horizontal total wind speed average at the surface</li> </ul>
Unravelling Plankton Adaptation in Global Oceans through the Analysis of Lipidomes
<p>This file contains all the data relevant to the manuscript titled “Unravelling Plankton Adaptation in Global Oceans through the Analysis of Lipidomes,” as well as all the code used to generate the data and the figures presented in the manuscript.</p>
Data files for "Genomic-to-space measurements reveal global ocean nutrient stress"
<p>Data files to be used with the following code: https://github.com/ljustick/genomic_to_space_nut_stress</p>
Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux
<p>This is dataset of global climate model simulation used in the paper "Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux" by Shimura et al. (2021)</p> <p>Followings are the explanation of data file.</p> <p>*** File naming rule ***<br> {data_group_name}_Exp{experiment_name}_TCnumber{tropical_cyclone_case_number}.nc</p> <p> data_group_name<br> - atm<br> - track</p> <p> experiment_name<br> - Wind<br> - Wave<br> - SlabO</p> <p> tropical_cyclone_case_number<br> - 001<br> - 002<br> ...<br> - 099<br> - 100</p> <p>*** Description on each data group ***<br> <br> atm: three dimentional atmospheric velocity data<br> - level: pressure levels for vertical atmospheric data<br> - longitude: Longitude<br> - latitude: Latitude<br> - velocity_u_component: averaged atmospheric eastward velocity</p> <p> track: data around tropical cyclone track<br> - time: UTC time (YYYYMMDDHH)<br> - longitude_center: Longitude of typhoon center<br> - latitude_center: Latitude of typhoon center<br> - central_pressure: typhoon central pressure<br> - maximum_surface_wind: typhoon maximum surface wind speed<br> - longitude_sfc: Longitude for surface data around typhoon center<br> - latitude_sfc: Latitude for surface data around typhoon center<br> - surface_wind_u_component: surface eastward wind around typhoon<br> - surface_wind_v_component: surface northward wind around typhoon<br> - sea_level_pressure: sea level pressure around typhoon<br> - latent_heat_flux: surface upward latent heat flux<br> - sensible_heat_flux: surface upward sensible heat flux<br> - time_atm: UTC time (YYYYMMDDHH) for atmospheric data<br> - level: pressure levels for atmospheric data<br> - longitude_atm: Longitude for atmospheric data around typhoon center<br> - latitude_atm: Latitude for atmospheric data around typhoon center<br> - velocity_u_component: 3d eastward velocity around typhoon<br> - velocity_v_component: 3d northward velocity around typhoon</p> <p> </p>
Examining the interaction between free-living bacteria and iron in the global ocean
<p>The model outputs, relevant data, and MATLAB scripts to reproduce the figures shown in the manuscript "Examining the interaction between free-living bacteria and iron in the global ocean" by Anh Le-Duy Pham, Olivier Aumont, Lavenia Ratnarajah, and Alessandro Tagliabue.</p> <p>Please contact the corresponding author Anh Pham (anh.pham@locean-ipsl.upmc.fr or anhlpham78@ucla.edu) for any questions.</p>
Global annual mean Absolute Dynamic Topography of permanently ice-free Oceans 1993 to 2020
<p>The text, data and figure disclosed are part of the manuscript “Intensification of the South Pacific Subtropical Gyre Circulation (1993–2020) Revealed by Satellite Altimetry” by Wolfgang Schneider, Freddy Hernández-Vaca, José Garcés-Vargas, and Iván Pérez-Santos, which was submitted to the Journal of Geophysical Research: Oceans</p> <p> </p> <p>The time series presented below were generated using E.U. Copernicus Marine Service Information, namely “Absolute Dynamic Topography” which was retrieved from the following Copernicus Marine Environment Monitoring Service (CMEMS) ftp site.</p> <p> </p> <p>ftp://my.cmems-du.eu/Core/SEALEVEL_GLO_PHY_L4_MY_008_047/cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.25deg_P1D/1993/01/</p> <p> </p>
Global Ocean particulate organic phosphorus, carbon, oxygen for respiration, and nitrogen (GO-POPCORN) data from Bio-GO-SHIP cruises
<p>Here, we present the Global Ocean Particulate Organic Phosphorus, Carbon, Oxygen for Respiration, and Nitrogen (GO-POPCORN) dataset with data from the recent Bio-GO-SHIP cruises between 2011 and 2020 supplemented with data from Arctic IERP cruises. The dataset contains 2581 paired measurements of particulate organic carbon, nitrogen, and phosphorus from 70°S to 73°N across all major ocean basins. The dataset also includes 965 measurements of <span>particulate chemical oxygen demand</span>. This new dataset is valuable for improving our understanding of how biological elemental stoichiometry plays a role in regulating both the marine nutrient cycles and the global carbon cycle.</p>
Data for "Global ocean pCO2 variation regimes: spatial patterns and the emergence of a hybrid regime"
<p>Model data for article "Global ocean pCO2 variation regimes: spatial patterns and the emergence of a hybrid regime".</p>
Subset of global model sea level data for "Challenges, Advances and Opportunities in Regional Sea Level Projections: the Role of Ocean-shelf Dynamics"
<p>Monthly sea surface height above the geoid data in NW European seas from six global simulations using the NEMO ocean model (https://www.nemo-ocean.eu/) for 1990 to 2009</p> <p><strong>ORCA0083_DFS_NWS_ssh_1990_2009, ORCA025_DFS_NWS_ssh_1990_2009, ORCA1_DFS_NWS_ssh_1990_2009,</strong> are the N006 simulation set created by Andrew Coward and the NOC Marine Systems Modelling team as used by:</p> <p>Baker et al 2022 Biological Carbon Pump Sequestration Efficiency in the North Atlantic: A Leaky or a Long-Term Sink? Global Biogeochemical Cycles <a href="https://doi.org/10.1029/2021GB007286">https://doi.org/10.1029/2021GB007286</a>,</p> <p>Wilson, C. <em>et al.</em> 2021 Significant variability of structure and predictability of Arctic Ocean surface pathways affects basinwide connectivity. <em>Commun. Earth Environ.</em> <strong>2</strong>, 164. <a href="https://doi.org/10.1038/s43247-021-00237-0">https://doi.org/10.1038/s43247-021-00237-0</a> (2021).</p> <p>These simulations are forced by the Drakkar Forcing Set 5.2 (DFS) and initialised at 1958, with a nominal 1/12, 1/4 and 1 degree resolution. See references for further model details.</p> <p><strong>ORCA025_JRA_NWS_ssh_1990_2009, ORCA025_JRA_tides_NWS_ssh_1990_2009, ORCA025_JRA_ShelfPhysics_NWS_ssh_1990_2009, </strong>are new simulations produced by Chris Wilson, James Harle and the Shelf Enabled NEMO team. All are forced by the JRA reanalysis, initialised in 1976.</p> <p><strong>ORCA025_JRA_NWS_ssh_1990_2009</strong> is a reference run based on GO9, an evolution of the Joint Marine Modelling Programme configuration described by Storkey et al 2018 UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geoscientific Model Development https://gmd.copernicus.org/articles/11/3187/2018/</p> <p><strong>ORCA025_JRA_tides_NWS_ssh_1990_2009</strong> adds explicit tides to this.</p> <p><strong>ORCA025_JRA_ShelfPhysics_NWS_ssh_1990_2009</strong> adds tides, Generic Length Scale Mixing and Multi-envelope vertical coordinates</p> <p>Details of these simulations can be found here:</p> <p>https://github.com/NOC-MSM/SE-NEMO </p>
Data and scripts (2) for Storkey et al, "Resolution dependence of interlinked Southern Ocean biases in global coupled HadGEM3 models", GMD (2024)
<p>================================================================<br> Data and scripts for producing plots from Storkey et al (2024):<br> "Resolution dependence of interlinked Southern Ocean biases in<br> global coupled HadGEM3 models"<br> ================================================================</p> <p>The plots in the paper consist of 10-year mean fields from the third <br>decade of the spin up and timeseries of scalar quantities for the first<br>150 years of the spin up. The data to produce these plots are stored<br>in the MEANS_YEARS_21-30 and TIMESERIES_DATA directories respectively.</p> <p>Note that due to the size limit on records on Zenodo, the 10-year mean <br>output from the N216-ORCA12 integration has been stored as a separate<br>record.</p> <p>Scripts to produce the plots are in SCRIPT, with section definitions<br>in SECTIONS. Bespoke plotting scripts are included in SCRIPT. They use<br>python 3 including the Matplotlib, Iris and Cartopy packages. The <br>plotting of the timeseries data used the Marine_Val VALSO-VALTRANS <br>package which is available here:</p> <p> https://github.com/JMMP-Group/MARINE_VAL/tree/main/VALSO-VALTRANS </p> <p>Much of the processing of the model output data was performed with the<br>CDFTools package, which is available here:</p> <p> https://github.com/meom-group/CDFTOOLS</p> <p>and the NCO package:</p> <p> https://web.mit.edu/course/13/13.715/nco-2.8.1/doc/</p>
Assessing the ocean carbon sink: Assimilation of temperature and salinity into a global ocean biogeochemistry model
<p>Data underlying figures in manuscript draft.</p>
Assessing Patterns of Metazoans in the Global Ocean using Environmental DNA
<p>Data for the above publication which is in RSOS. Code is available here, https://github.com/ngeraldi/Global_ocean_genome_analysis. The 5 files include the amplicon data (Malaspina and Tara) and all metagenome data is in DMAP_biomass_apr19 (it is not biomass, it is metagenome data). Global layers contain the metadata- april18 is amplicon data and genome is the metagenome data.</p> <p>Abstract:</p> <p>Documenting large-scale patterns of animals in the ocean and determining the drivers of these patterns is needed for conservation efforts given the unprecedented rates of change occurring within marine ecosystems. We used existing datasets from two global expeditions, <em>Tara </em>Oceans<em> </em>and <em>Malaspina</em>, that circumnavigated the oceans, and sampled down to 4000 meters to assess metazoans from eDNA extracted from seawater. We describe patterns of taxonomic richness within metazoan phyla and orders based on metabarcoding and infer relative abundance of phyla using metagenome datasets, and relate these data with environmental variables. Arthropods had the greatest taxonomic richness of metazoan phyla at the surface, while cnidarians had the greatest richness in pelagic zones. Half of the marine metazoan eDNA from metagenome datasets was from arthropods, followed by cnidarians and nematodes. We found that mean surface temperature and primary productivity were positively related with metazoan taxonomic richness. Our findings concur with existing knowledge that temperature and primary productivity are important drivers of taxonomic richness for specific taxa at the ocean’s surface, but these correlations are less evident in the deep ocean. Massive sequencing of eDNA can improve understanding of animal distributions, particularly for the deep ocean where sampling is challenging.</p>
The role of external inputs and internal cycling in shaping the global ocean cobalt distribution: insights from the first cobalt biogeochemical model
<p>Model output for cobalt biogeochemistry model on ORCA2 grid.</p>
The Antarctic ice sheet iron source : a sensitivity study with a global ocean model
<p>Contains model data and freshwater fluxes from icebergs and ice shelves (used as forcing file to represent the Fe supply from the Antarctic ice sheet) of the study "Sensitivity of ocean biogeochemistry to the iron supply from the Antarctic ice sheet explored with a biogeochemical model", submitted to Biogeosciences (EGU)</p>
Eulerian modelling of the three-dimensional distribution of seven popular microplastic types in the global ocean dataset
<p>Dataset for the paper "Eulerian modelling of the three-dimensional distribution of seven popular microplastic types in the global ocean" by A. S. Mountford and M. A. Morales Maqueda.</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_con.nc<a href="https://zenodo.org/api/files/c6c9cbac-d5db-452a-aade-fd852db07351/ORCA2_5d_00010101_00011231_ptrc_T_con.nc"> </a> - control experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_30m.nc - 30 m year<sup>-1</sup> piston velocity sensitivity experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_90m.nc - 90 m year<sup>-1</sup> piston velocity sensitivity experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_50.nc - neutrally buoyant sensitivity simulation (year 50)</p> <p>plastic_1_ts.nc - positively buoyant time series</p> <p>plastic_2_ts.nc - neutrally buoyant time series</p> <p>plastic_3_ts.nc - negatively buoyant time series</p> <p>plastic_input_ORCA2.nc - plastic input data file</p> <p>ORCA2_5d_00010101_00011231_grid_U.nc & ORCA2_5d_00010101_00011231_grid_V.nc - ocean velocity files</p>
Data for "Trend and variability in global upper-ocean stratification since the 1960s"
<p>Abstract of associated paper: Many studies on future climate projection point out that, with progressing of global warming, upper-ocean stratification will strengthen over this century and consequently global-averaged ocean primary production will decrease. Observed long-term changes in the stratification to date, however, still show large uncertainties of the change itself and its driver. Focusing on the vertical difference in the emergence of the global warming signals, we used only observational profiles to describe the spatiotemporal characteristic of long-term trend and variability in the upper-ocean stratification. Rapid strengthening of the stratification (defined as the density difference between the surface and 200 m depth) since the 1960s was detected over most of the global ocean. Although the global average increase over 58 years (1960–2017) corresponds to 3.3–6.1% of the mean stratification, these strengthening trends considerably change depending on the regions. In addition to the well-documented explanation of strengthening stratification, namely that the surface intensification of global warming signal, we found that changes in subsurface temperature and salinity stratification associated with changes in atmospheric/ocean circulations and the global water cycle significantly contribute to the long-term change in the stratification and setting its regional difference. In mid- and high-latitude ocean of the northern hemisphere, the long-term trend in density stratification has noteworthy seasonality, which shows faster increase in boreal summer than that in winter. From the detrended time series, interannual variabilities correlated with a particular climate mode are detected in several ocean regions, suggesting that these variabilities are mainly driven by associated sea surface temperature variation.</p>
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.