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955 results for “Ocean data”
Data and code for gmd-2023-113 "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"
<p>Data and code for the paper "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"</p> <p>includes: </p> <p>The model is Community Earth System Model (v1.2.1) (provided by www.cesm.ucar.edu)</p> <p>Data assimilation code is initially provided by Data Assimilation Research Testbed (DART) (https://dart.ucar.edu/), some modifications are made to enable parameter estimation function of ocean background vertical diffusivity coefficients. And the programs and scripts for deal with OISST and EN4 profiles are also developed.</p> <p>The parameter sensitivity experiment results are saved as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012.nc">sensitive2008-2012.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012salt.nc">sensitive2008-2012salt.nc</a> for temperature and salinity, respectively. And the python script to draw the results is </p> <p>The state estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>The parameter estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>the estimated paremeter ensemble is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/parameters.nc">parameters.nc</a></p> <p>the python script for comparing the SE and PE results is <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/plot_analysis.py">plot_analysis.py</a></p> <p>the nino3.4 indices computed by the forecast experiment is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/fcst_correlation.nc">fcst_correlation.nc</a></p> <p> </p>
The Antarctic sea ice reconstruction (CMST-South) based on the optimized Data Assimilation System for the Southern Ocean
<p>The wealth of historical sea ice concentration (SIC) observations, coupled with their extensive spatial coverage, renders them indispensable for the reconstruction of long-term Antarctic sea ice variability. However, recent studies have pointed out the presence of significant uncertainties in certain aspects of Antarctic sea ice reanalyses obtained from assimilating SIC. Notably, while previous studies on ocean data assimilation have already demonstrated the significance of optimizing model-dependent parameters for assimilating oceanic observations, this aspect has received limited attention in current sea ice data assimilation studies. As a result, whether optimizing model-dependent parameters can enhance the effectiveness of assimilating SIC remains an open question. Thus, we address this gap by refining the model-dependent parameters of Data Assimilation System for the Southern Ocean (DASSO), including the development of a latitude-dependent localization scheme and the objective estimation of observation error variance of SIC which takes into account both measurement errors and representation errors.</p> <p>Here, the monthly anomalies in Antarctic sea ice extent and volume (1980 -2018) are uploaded which is produced by the optimized Data Assimilation System for the Southern Ocean (DASSO) with assimilating SIC. Besides, a 13-month moving mean is applied to monthly anomalies to focus on the low-frequency variability of Antarctic sea ice.</p>
ADCP Data from the Chukchi Sea, Arctic Ocean, acquired during the R/V Marcus G. Langseth expedition MGL1112
<p>This is a processed acoustic Doppler current profiler (ADCP) dataset, provided by Andrew Frambach and Jules Hummon, acquired in September 2011 during the MGL1112 cruise at Chukchi Borderland, Arctic Ocean.</p>
Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases
<p>These data were used for the development of the paper "<strong>Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases</strong>". Especifically, we added ecological and environmental data that were used for modeling.</p>
SST_front_data: ocean thermal fronts detected by the Cayula and Cornillon SIED algorithm
<p>This dataset includes the post-processed data and a demo MATLAB script used for the paper titled "Global trends of fronts and chlorophyll in a warming ocean"</p> <p><strong>SST_FRONT_data.zip</strong> contains maps of sea surface temperature (SST) fronts detected by the Cayula and Cornillon single image edge detection algorithm over global ocean warming hotspot regions and covering the period 2003-2020. The original data was obtained from NASA OB.DAAC MODIS sea surface temperature (SST) product (MODIS Aqua Level 3 SST MID-IR 8 Day 4km Nighttime V2019.0: https://podaac.jpl.nasa.gov/dataset/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0?ids=&values=&search=MODIS%20Aqua&provider=POCLOUD). </p> <p>SST_FRONT_data.zip also contains <strong>Fdens_Ffreq_Fstre_example.mlx</strong>, which<strong> </strong>is a MATLAB live script showing how to compute metrics of fronts based on frontal maps: frontal frequency (Ffreq), frontal density (Fdens), and frontal strength (Fstre). </p> <p><strong>Fdens_Ffreq_Fstre_example.pdf</strong> is intended for quick viewing of the script above.</p> <p> </p>
Data and analysis scripts for: Recent acceleration in global ocean heat accumulation by mode and intermediate waters
<p>The folder contains the MATLAB code and data to re-create Figures 1-9 and S1-3 within the publication by <em>Li, Z., England, M. H., & Groeskamp, S. Recent acceleration in global ocean heat accumulation by mode and intermediate waters, Nature Communications</em>, 2023.</p>
MCR LTER: Coral Reef: Ocean Currents and Biogeochemistry: Moored Thermistor String Data - CBYTS
A vertically moored thermistor string sampled year-round on the reef at Cook's Bay in Moorea, French Polynesia. Sampling began in 2005 and ended in August 2011. All data have been interpolated onto a 20 min grid. Thermistors were spaced vertically along the mooring line 4, 8, 12, 16, and 20 meters above the bottom. Pressure measurements are also provided from two of the instruments typically located at 4 and 20 meters above the bottom.
MCR LTER: Reef Topography data from Duvall et al., JGR Oceans 2019
This archive contains natural coral reef topography data from the southeast coast of Mo’orea, French Polynesia and idealized reef topography generated with a fractional Brownian motion (fBm) algorithm. These data were used to understand and compare different metrics for quantifying coral reef roughness. These data relate to this publication: Duvall, M. S., J. L. Hench, and J. H. Rosman, in press, Collapsing complexity: metrics to quantify multi-scale properties of reef topography. To appear in Journal of Geophysical Research (Oceans). This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
SBC LTER: Ocean: Currents and Biogeochemistry: Moored CTD and ADCP data from Purisima Mooring (PUR), 1999-2016
ADCP (Currents), CTD (Hydrography) and Optics data (Fluorescence, Beam Attenuation and Volume Scattering Function) were collected at La Purisima (site ID: PUR), north of Point Conception. Data have been interpolated to a 20 minute interval. ADCP data are binned at a 1.0 meter interval, measured as height from the bottom to a maximum of 16 bins. All bins may not be filled, and in some cases, data from bins technically above the surface are included. VSF data are available at angles, 100, 125 and 150 degrees. CTD parameters include Pressure, Temperature, Conductivity, Salinity, Density and Fluorescence. The CTD array is located approximately 4.5 meters from the surface, and there are additional temperature thermistors near the CTD array, at the bottom, and mid way between these two.
They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1990-2002 (2 of 2)
<p>Supporting data for Kelly et al.: They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean (Earth's Future, submitted)<br> <br> Trajectories saved by year of release in the Bering Strait. All months from that year are included in the same file, with the first 1000 trajectories corresponding to January release, second 1000 from February release, and so on. <br> <br> Due to the size of files, this is split into two uploads. Part 1 covers 1970-1989 releases, 1990 onward is saved in Part 2. </p>
They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1970-1989 (1 of 2)
<p>Supporting data for Kelly et al.: They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean (Earth's Future, submitted)<br> <br> Trajectories saved by year of release in the Bering Strait. All months from that year are included in the same file, with the first 1000 trajectories corresponding to January release, second 1000 from February release, and so on. <br> <br> Due to the size of files, this is split into two uploads. Part 1 covers 1970-1989 releases, 1990 onward is saved in Part 2. </p>
Code and data for "Contrasting upper and deep ocean oxygen response to protracted global warming," by Frölicher et al., Global Biogeochemical Cycles, 34, e2020GB006601: https://doi.org/10.1029/2020GB006601
<p>This file contains the data and python/NCL scripts that have been used for the analysis in this paper. </p>
Data, Sensitivity of 21st-century projected ocean new production changes to idealized biogeochemical model structure
<p>Data for reproducing figures in journal article submitted to Biogeosciences in December 2020.</p> <p>Data generated from global 1-degree simulations of the CESM in an ocean-ice configuration.</p> <p>NP model by Brett. See 10.5281/zenodo.4361705 for code for NP model and to use this dataset to recreate paper figures.</p>
Model Data for "Increased Ocean Heat Convergence into the High Latitudes with CO2-Doubling Enhances Polar-Amplified Warming."
<p>This is the data repository for the following published study:</p> <p>Singh HA, Rasch PJ, and Rose BEJ. "Increased Ocean Heat Convergence into the High Latitudes with CO<sub>2</sub>-Doubling Enhances Polar-Amplified Warming", Geophysical Research Letters, Oct 2017, doi: 10.1002/2017GL074561.</p> <p>Please see 'README.txt' for further details on the data files included.</p>
sea-surface temperature proxy data (TEX86 and UK'37) from Ocean Drilling Program Site 1168
<p>These 2 data files contain the TEX86 and UK'37 sea surface temperature proxy data from Ocean Drilling Program Site 1168, covering the Eocene to recent (35–0 Ma). These were updated compared to previous versions, wherein some alkenone data was omitted.</p>
Summarised contextual data about metabarcoding Tara Oceans samples (2009-2013)
<p>Tab-separated values table describing the metabarcoding samples from the expedition Tara Oceans (2009-2013).</p> <p>Information such as depth, time, geographic position, size fraction, collected from <a href="https://pangaea.de/">Pangaea</a>, are listed in context_general tables. In context_stat tables, you will find a selection of physico-chemical parameters. Tara_Oceans_Pangaea_context.rds gathers all the data collected from Pangaea in a single R object.</p> <p>These tables have been built using the code here: <a href="https://gitlab.com/tara-and-friends-euk-metab/tara-oceans-metab-context/-/tree/v1.1.1" target="_blank" rel="noopener">https://gitlab.com/tara-and-friends-euk-metab/tara-oceans-metab-context/-/tree/v1.1.2</a> (v1.1.2).</p> <p>In this version 16S metabarcoding samples missing in previous versions were added in context_general.* and context_sats.*</p>
Data used in "The Complex Role of Storms in Modulating Air-Sea CO2 Fluxes in the sub-Antarctic Southern Ocean"
<p>The data included in this repository were used to generate the figures for the paper "The Complex Role of Storms in Modulating Air-Sea CO2 Fluxes in the sub-Antarctic Southern Ocean" in Geophysical Research Letter.</p> <p>Abstract:</p> <p>"The intra-seasonal CO<sub>2</sub> flux (FCO<sub>2</sub>) variability across the Southern Ocean is poorly understood due to sparse observations at the required temporal and spatial scales. Twinned Waveglider-Seaglider experiments were used to investigate how storms influence FCO<sub>2</sub> through both the gas transfer velocity (k<sub>w</sub>) and the air-sea gradient in partial pressure of CO<sub>2</sub> (ΔpCO<sub>2</sub>) in the sub-Antarctic zone. Winter-spring storms caused ΔpCO<sub>2</sub> to weaken (by 15-55 μatm) due to mixing/entrainment and weaker stratification. This response in ΔpCO<sub>2</sub> was in phase with k<sub>w</sub> resulting in a counteractive weakening in FCO<sub>2</sub> (by 6.6 - 26.5% per storm), despite the wind-driven increase in k<sub>w</sub>. Stronger stratification during summer explained the weaker sensitivity of ΔpCO<sub>2</sub> to storms, instead its thermal drivers dominated the ΔpCO<sub>2 </sub>variability. These results highlight the importance of observing synoptic-scale variability in ΔpCO<sub>2</sub>, the absence of which may propagate significant biases to the mean annual FCO<sub>2</sub> estimates from large-scale observing programmes and reconstructions."</p> <p>The data collected from the Wave Glider, such as the concentration of CO<sub>2</sub> in the atmosphere (xCO<sub>2air</sub>) and in the ocean (xCO<sub>2sea</sub>), surface temperature and salinity were used to calculate the different parameters of the bulk CO<sub>2</sub> flux formula (FCO<sub>2</sub> = k<sub>w</sub> x ko x ΔpCO<sub>2</sub>). Note that the meteorological weather station of one of the Wave Gliders was faulty and the wind speed, wind direction and wind stress data was replaced by hourly ERA5 data provided by ECMWF available at <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a>. </p> <p>The temperature, pressure and salinity data collected by the Seaglider were used to calculate the Mixed Layer Depth and the Brunt Vaisala Frequency of the first 300m of the ocean.</p>
Storyline Simulations Data for the paper Athanase et al.: Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean
<p>Data used for producing the Figures in the paper entitled "Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean", Athanase et al. (Communications Earth & Environment).</p> <p>The AWI-CM-1-1-MR free runs are available in the Earth System Grid Federation (ESGF) data nodes (https://esgf-data.dkrz.de/search/cmip6-dkrz/). The ERA5 reanalysis data used in the paper can be accessed from the European Centre for Medium-Range Weather Forecasts (ECMWF; https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). Here, we provide data from the nudged storyline simulations carried out with the AWI-CM-1-1-MR coupled climate model.</p> <p>Parameters naming convention:</p> <p>- Sea Surface Temperature ("tos").</p> <p>- Radiative Fluxes ("radiations"), including net surface heat flux ("qnet"), net surface thermal radiation ("trads"), net surface solar radiation ("srads"), latent heat flux ("ahfl"), sensible heat flux ("ahfs").</p> <p>- Low Clouds Cover ("lcc").</p> <p>- Mixed Layer Depth ("mlotst").</p> <p>- Surface Air Temperature ("tas").</p> <p>- 10 m winds ("u10","v10").</p> <p>All data is provided as the 5-member ensemble mean from the nudged storyline simulations. Data is provided for the storyline simulations of the summer 2019 Northeast Pacific marine heatwave, in different background climate conditions: preindustrial ("PI"), present-day ("PD"), and +4°C warmer world ("4K"). </p> <p> </p>
Data and code for "Extreme and compound ocean events are key drivers of projected low pelagic fish biomass"
<p>This repository provides the data and code for the paper "Extreme and compound ocean events are key drivers of projected low pelagic fish biomass". Almost all data required to produce the figures in this study are provided. However, not all raw data are provided, because of too large file sizes. For more information, please contact natacha.legrix@unibe.ch</p> <p>In Version 2, an error has been corrected in the computation of the grid cell area, which significantly affected values in Fig. A1.</p>
Data from: Mechanisms underpinning the net removal rates of dissolved organic carbon in the global ocean
<p>With almost 700 Pg of carbon, marine dissolved organic carbon (DOC) stores more carbon than all living biomass on Earth combined. However, the environmental controls behind the persistence and the spatial patterns of DOC concentrations on basin scale remain largely unknown, precluding quantitative assessments of the fate of this large carbon pool in a changing climate. We present the first global dynamic DOC model in agreement with more than 40,000 DOC observations, in which a feedback between DOC and picoheterotrophs is explicitly included (model of MICrobial-DOC interactions, MICDOC). This dataset contains model output and related information for a global model simulation in which a colimitation of macronutrients and organic carbon on microbial DOC uptake is implemented and explains >70% of the global variation of observed DOC concentrations. It provides the model output, source code and meta data for the simulations performed for the publication (doi: 10.1029/2023GB007912) "Mechanisms Underpinning the Net Removal Rates of<br>Dissolved Organic Carbon in the Global Ocean" by Lennartz et al.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.