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234 results for “Global Ocean”
360-info/tracker-ocean-temperatures: Monthly global ocean surface temperatures: v2024-10-22
<p>Tracks the monthly average sea surface temperatures using the <a href="https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html">OISST v2</a> dataset, created by NASA's <a href="https://psl.noaa.gov">Physical Sciences Laboratory</a>.</p><p>OISST updates both daily and monthly (we use the monthly updates here). The dataset <a href="https://www.ncei.noaa.gov/products/optimum-interpolation-sst">blends sea surface temperature observations</a> from satellites, ships, buoys and Argo floats.</p>
Ensemble projections (+ uncertainties) of contemporary (2012-2031) and future (2081-2100) mean annual plankton/phytoplankton/zooplankton species diversity (and species turn-over in time) for the global surface open ocean.
<p><em><strong>Gridded spatial fields (raster objects) containing the species distribution models (SDMs) projections of mean annual plankton total plankton, phytoplankton and zooplankton species diversity from Benedetti et al. (2021). </strong></em></p> <p>The present .grd file ('rasterStack' object in R) contain the fields of mean annual surface plankton/phytoplankton/zooplankton species diversity for the contemporary (2012-2031) and future (2081-2100) conditions of the global open ocean (i.e., data underlying those maps in Figure 1 and Figure 3 of Benedetti et al., 2021). Layers quantifying the uncertainty (i.e., the variablity across models projections estimated through the standard deviation) in ensemble projections were also added (i.e., data underlying the maps in Supplementary Figure 4). See the Methods section of Benedetti et al. (2021) for a full description of the methodology and the ensemble SDMs forecasting framework. The raster layers follow the 1°x1° cell grid of the World Ocean Atlas (https://www.ncei.noaa.gov/).</p> <p>In short, we empirically modelled the monthly and mean annual diversity patterns stemming from the distribution of 860 plankton species (336 phytoplankton, 524 zooplankton) spanning 13 phyla, 71 orders and 324 genera through an ensemble approach based on SDMs. The considered species cover a wide range of traits and functions, representing 10 major plankton functional groups (PFGs; three phytoplankton and seven zooplankton groups). We compiled the species occurrence records from various data sources (available here: https://zenodo.org/record/5101349#.YO7Dqm469lM) and aggregated them onto a monthly-resolved 1°x1° grid, excluding observations from regions where the seafloor is shallower than 200 m. We matched these binned open ocean records with observation-based climatologies of environmental predictors (temperature, dissolved oxygen concentration, solar irradiance, macronutrients concentration, chlorophyll a concentration) that reflect the climatic and biogeochemical conditions of the surface open ocean. Four types of SDMs (generalized linear models, generalized additive models, artificial neural networks, and random forests) were fitted to model the species’ current environmental habitat suitability patterns. For each SDMs, we used four alternative pools of predictors. Assuming niche conservatism, we projected each of the 16 resulting species-level habitat suitability models into the future using outputs from five ESMs belonging to the Coupled Model Intercomparison Project 5 (CMIP5) that were forced by the Representative Concentration Pathway 8.5 (RCP8.5) scenario of high greenhouse gas concentrations. To this end, we first computed the modelled monthly climatologies of the selected predictors for the 2012-2031 and 2081-2100 periods, and derive the future monthly anomalies from the differences between these two time periods. These anomalies were added to the observation-based monthly climatologies (i.e., those used to train the SDMs) to estimate the future environmental conditions of the ocean, and projected the SDMs in these future conditions. Finally, we estimated the mean annual present and future alpha diversity (species richness; SR) and beta diversity (species turnover through time) patterns for both trophic levels, for each cell, from the ensemble of SDMs. SR ensembles are estimated as the sum of all species’ habitat suitability patterns averaged across all 80 possible combinations (i.e., "ensemble members") of SDMs (n = 4), ESMs (n = 5) and predictor pools (n = 4). To assess the uncertainties of our diversity projections based on the ensemble members, we compute the interquartile range of the 80 ensemble members SR projections. We calculate species turnover as the change in mean annual species composition between present and future time based on Jaccard’s dissimilarity index and by decomposing this total turnover into the true species turnover (ST, also known as species replacement) and the nestedness (SR change) components. Numerous tests are conducted to ensure the robustness of the results with regard to the spatially and temporally highly uneven sampling effort as well as with regard to the relative role of different predictors.</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.</strong></p>
A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission example data
<p>These files contain the Confluence pipeline outputs, prior information (SOS) and Simulated SWOT shape files from the example in the "A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission" manuscript. </p>
Global Ocean Heat Content Anomalies based on Argo data
<p><strong>NOTE for users: please use the latest version of the product at https://zenodo.org/doi/10.5281/zenodo.10182972. </strong>Ocean Heat Content Anomalies (OHCA) are calculated (during 2005-2022) subtracting the mean over the period 2005-2021 from the monthly time series. Yearly OHCA time series are then calculated. OHC fields are mapped using locally stationary Gaussian processes with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). In the present version, mapping is done in latitude and longitude with monthly subsets of data (a future version will add time to the mapping). Mapping is done separately for different vertical sections. Different vertical sections are combined to estimate: 1. Global OHC timeseries (e.g., for level 0-2000m: GCOS_0000_2000_OHCA_J_m2_oc, for OHC in J/m2; GCOS_0000_2000_OHCA_ZJ, for OHC in ZJ; the attribute “GCOS_area” is included for both variable types in the netcdf file and it tells the corresponding surface area); 2. Volume averaged temperature anomaly (global) timeseries (e.g., for level 0-2000m: GCOS_0000_2000_vol_ave_temp_anom, in degC; the attribute “GCOS_volume'' is included for this variable type in the netcdf file and it tells the corresponding volume). Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included.</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>
Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)
<p>Model output of the global eddy-rich configuration used in the Geoscientific Model Development publication: "Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)" </p> <p>Abstract: This paper describes the global eddying ocean-sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16° and 98 vertical levels, and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment.<br> To access the accuracy of simulated 3D ocean fields, and highlight the relative benefits of mesoscale activities, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean-sea ice component of the fully coupled CMCC climate model. <br> The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling effort, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions.<br> </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>
Final results from McCoy et al., 'Global Observations of Submesoscale Coherent Vortices in the Ocean', submitted to Progress in Oceanography.
<p>Submesoscale coherent vortices (SCVs) are small-scale, subsurface eddies that are ubiquitous in the ocean. Observations suggest that they efficiently trap and transport water, nutrients, and other properties thousands of kilometers away from their formation regions. However, the weak sea-surface signature restricts SCV observations to mostly chance encounters with shipboard subsurface instrumentation. Thus, the global occurrence, properties, and generation frequency of SCVs remain poorly constrained. Here we present results from a new algorithm used to identify SCVs from Argo float data, applied to roughly 2 million profiles conducted globally from August 1997 to January 2020.</p> <p>After application of the SCV detection algorithm to the global Argo array, we identify 2501 casts piercing spicy-core SCVs (those with anomalously hot and salty water mass characteristics), and 1583 casts piercing minty-core SCVs (anomalously cold and fresh cores) over more than 20 years of available data. The Matlab file 'final_individual_scvs.mat' contains various data for each SCV identified.</p> <p>By grouping detections from consecutive Argo casts, we are also able to record 383 spicy-core SCV time-series and 169 minty-core SCV time-series. The Matlab file 'final_timeseries_scvs.mat' contains the data for these time-series. </p> <p>For a more detailed description of each Matlab file, please see 'README.rtf'. </p> <p>Reach out to Daniel McCoy (dmccoy801@gmail.com) or Daniele Bianchi (dbianchi@atmos.ucla.edu) for inquiries. </p>
The Impact of a Pressurized Regional Sea or Global Ocean on Stresses on Enceladus: Numerical models
<p>Comsol Multiphysics models solving the stress field in Enceladus' ice shell when a regional sea of global ocean is pressurized.</p> <p>Model parameters are included as part of the file name according to the template EnceladusT<em>t</em>D<em>d</em><em>Label</em>.mph with</p> <ul> <li><em>t</em> is the ice shell thickness</li> <li><em>d</em> is the thickness of the south polar sea or indentation</li> <li><em>Label </em>indicates model configuration <ul> <li><em>Fixed</em>: The base of the ice shell (outside the south polar sea) is in contact with the core with a no-slip boundary condition</li> <li><em>Roller</em>: The base of the ice shell (outside the south polar sea) is in contact with the core with a free-slip boundary condition</li> <li><em>Ocean</em>: The base of the ice shell is floating with a constant pressure condition; there is a single indentation at the South pole</li> <li><em>North</em>: The base of the ice shell is floating with a constant pressure condition; there are indentations at both poles, with the north pole indentation having half the thickness of the South pole indentation</li> </ul> </li> </ul> <p>There are two solved datasets in each model. The first uses a default value of the ocean angle (40°). The second results from a parameter sweep in which the sea angle varies systematically in increments of 2°.</p>
Time-varying global energy budget since 1880 from a reconstruction of ocean warming
<p>This dataset was produced as part of the research presented in the paper "Time-varying global energy budget since 1880 from a reconstruction of ocean warming" published in PNAS. For detailed methodology, analysis, and interpretation of the data, please refer to the original publication: 10.1073/pnas.2408839122.</p>
CIGAR-CS Global Ocean Reanalysis 1961-2022 Ocean Heat Content
<p>Dataset describing the <strong>CIGAR-CS (v1) </strong>reanalysis Ocean Heat Content.</p> <p><strong>CIGAR</strong>: The CNR ISMAR Global Historical Reanalysis (<a href="http://cigar.ismar.cnr.it">http://cigar.ismar.cnr.it</a>)</p> <p><strong>CS</strong>: Contemporary Stream</p> <p>CIGAR-CS is an ensemble ocean reanalysis with 32 members, covering the period from 1959 to real-time, and based on the NEMO4 model, a variational data assimilation scheme with variational quality control of in-situ profiles and time-varying background-error covariances, a surface correction scheme of air-sea fluxes, a deep-ocean bias correction scheme, and an advanced ensemble generation scheme with stochastic physics and perturbation of input datasets.</p> <p>It includes yearly mean files for each of the <strong>32 ensemble members</strong> from 1961-2022 for these selected variables:<br> - Ocean heat content (full column)<br> - Temperature analysis increments<br> - Surface net air-sea heat fluxes</p> <p>Heat fluxes and analysis increments are provided for potential use in ocean warming attribution studies.</p> <p>To ease the use of the OHC data, all fields are remapped from the irregular ORCA1 tripolar grid (1/3deg to 1deg of spatial resolution) to a regular 0.5degx0.5deg grid through bilinear interpolation.</p> <p>(Note: all diagnostics in the reference paper were computed on the native irregular grid; possible differences, therefore, may exist and are due to the errors introduced by the interpolation)</p>
Deep learning reveals hotspots of global oceanic oxygen changes in the 21st century
<p>This is a global ocean-scale dissolved oxygen dataset derived from a deep learning method using hydrometeorological and biogeocheical driver factors. The dataset provides yearly estimates of dissolved oxygen concentration during 2003 to 2020 throughout the whole water column, with a spatial resolution of 0.25° × 0.25°. The unprecedented high-resolution dataset provides information on dissolved oxygen spatial distribution and temporal trend, making it a significant contribution to understanding global warming and anthropogenic stressors. </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>
Data for "The role of ocean mesoscale variability in air-sea CO2 exchange: a global perspective"
<p>Processed model data for article "The role of ocean mesoscale variability in air-sea CO2 exchange: a global perspective"</p>
Biogeochemical river inputs for global ocean models (RivR2O)
<h2><strong>1. General Description</strong></h2> <p>The global biogeochemical riverine export dataset (RivR2O) uploaded here is a synthesis product for yearly means of preindustrial C, N and P exports to the ocean and their historical evolutions, which are ready-to-use for global ocean models. They will serve as biogeochemical river inputs in the River-2-Ocean Model Intercomparison Study (R2OMIP). The files cover >10000 global catchments which can be read as lists with coordinates, or as gridded netcdf files (0.25°X0.25°). They cover the compounds DIC, DOC, POC, DIP and DIN. The assumed pre-industrial era is assumed to be pre-1900, whereas historical data will cover 1901-2020. </p> <p>Please site the dataset as: </p> <p>Lacroix, F., Liu, M., Ma, M., Resplandy, L., Beusen, A., Hauck, J., Lennartz, S., Li, Y., Tian, H., & Regnier, P. (2024). Biogeochemical river inputs for global ocean models (RivR2O) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13799103" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13799103</a></p> <h3><strong>1.1. Preindustrial inputs and their transformations</strong></h3> <p>The files are for preindustrial river inputs can be downloaded as netcdf (<strong>r2o_riverinputs_preindustrial.nc</strong>), or as catchment lists (DIC,DOC,POC,DIN: <strong>riverexports_list_CN.csv</strong> , DIP: <strong>riverexports_list_P.csv</strong>) with given coordinates. They quantify yearly means for every catchment without a significant anthropogenic perturbation. They were constructed in the following ways:</p> <p><strong>DI</strong><strong>C, DOC, POC</strong></p> <p>Preindustrial DIC, DOC and POC were obtained by subtracting the estimated anthropogenic perturbations for every catchment, which were determined for the 1901-2020 time period by Tian et al. (2023), from the synthesis of present-day exports by Liu et al. (2024). We further accounted for a net DOC source in the tropics (+0.07 Pg C yr-1), and a source in the Southern Hemisphere (+0.01 Pg C yr-1) from estuaries and coastal vegetated ecosystems (including submerged) based on Regnier et al. (2022). Note that in the study, Northern Hemisphere lateral transfers of DOC due to estuaries and coastal vegetation are estimated to approximately zero. A fraction of POC was also removed from the dataset due to models misrepresenting burial on shelf and the remaining fraction (recycled POC) should be added to the semi-refractory DOC pool (see protocol). DIC inputs from groundwater discharge (0.016 Pg C yr-1) were distributed globally homogeneously at every river mouth. Globally, this then amounts to a total of 0.51 Pg C yr-1 of DIC, 0.35 Pg C yr-1 of DOC and 0.095 Pg C yr-1 of POC of available C export to the ocean over the preindustrial time period. </p> <p><strong>DIN </strong></p> <p>The DIN product averages over three river N exports models (ORCHIDEE-NLAT: Ma et al., in review; DLEM: Yang et al., 2015; Tian, pers. Com., IMAGE-GNM: Beusen et al., 2015, 2016) for every catchment. The resulting preindustrial DIN load to the ocean is 11 Tg N yr-1. In addition, labile DON is accounted here as DIN (9 Tg N yr-1) based on the ratio C:N of 2583:103 from labile DOC given above (See R2O MIP protocol). This in total amounts to 20 Tg DIN yr-1 inputs to the ocean in the dataset.</p> <p><strong>DIP</strong></p> <p>The DIP product averages catchment estimates from IMAGE-GNM (Beusen et al., 2016) and Lacroix et al. (2020). The resulting preindustrial DIP load to the ocean is 2.28 Tg P yr-1. In addition, we account for labile DOP as DIP here (0.19 Tg P yr-1) based on the C:P ratio of 2583:1 (See R2O MIP protocol). This in total amounts to 2.47 Tg DIP yr-1 inputs to the ocean in the dataset.</p> <h3><strong>1.2. Anthropogenic Perturbation (1901-2024)</strong></h3> <p>The river input files from 1901 can be downloaded as a zip file (<a href="https://zenodo.org/api/records/14266183/draft/files/r2o_river_inputs_1901_2024.zip/content" target="_blank" rel="noopener noreferrer">r2o_river_inputs_1901_2024.zip</a>), which contains a netcdf files for every year of the time series (1901-2024) as rivr2o_riverinputs_{year}.nc. E.g. for 1901 -> rivr2o_riverinputs_{year}.nc </p> <p><strong>DI</strong><strong>C, DOC, POC</strong></p> <p>Preindustrial DIC, DOC and POC were obtained by interpolating linearly the estimated anthropogenic perturbations for every catchment, which were determined for the 1901-2024 time period by Tian et al. (2023), to the present-day exports by Liu et al. (2024). Based on Regnier et al. (2022), we assumed no lateral transfers of DOC due to estuaries and coastal vegetation for the present day. The same fraction of POC was also removed from the dataset due to models misrepresenting burial on shelf and the remaining fraction (recycled POC) should be added to the semi-refractory DOC pool (see protocol). DIC inputs from groundwater discharge (0.016 Pg C yr-1) were distributed globally homogeneously at every river mouth. Globally, this then amounts to a total of 0.53 Pg C yr-1 of DIC, 0.30 Pg C yr-1 of DOC and 0.12 Pg C yr-1 of POC of available C export to the ocean over the 2011-2020 period.</p> <p><strong>DIN </strong></p> <p>The DIN product averages over three river N exports models (ORCHIDEE-NLAT: Ma et al., in review; DLEM: Yang et al., 2015; Tian, pers. Com., IMAGE-GNM: Beusen et al., 2015, 2016) for every catchment. The total amounts to 30.03 Tg DIN yr-1 inputs to the ocean in the dataset for the 2011-2020 average (including inputs from labile DON).</p> <p><strong>DIP</strong></p> <p>The DIP product averages catchment estimates from IMAGE-GNM (Beusen et al., 2016) and Lacroix et al. (2020). This in total amounts to 4.92 Tg DIP yr-1 inputs to the ocean in the dataset.</p> <h2><strong>2. Use for modelers within the </strong><strong>R2O MIP </strong></h2> <p>We only briefly describe most important information on how to apply the river input data here and refer to the official R2O MIP protocol for more detail on our general simulation guidelines.</p> <ul> <li>We firstly recommend the addition of a terrestrial dissolved organic carbon pools in the ocean models: tDOC semi-labile (DOC_sl). Their only source should be that of the terrestrial inputs given here, it should be degraded with a first order constant of k_sl = 1 / 1.5yr (based on Hansell et al., 2012). The other tDOC compound given in the dataset, tDOC labile (tdoc_l), is assumed to be rapidly degraded and should therefore be added to the ocean model DIC pool.</li> <li>The inputs should be added to the closest ocean model grid points where the ocean model has freshwater inputs. Note that the inputs are given as 10^6 C/N/P per year, and this should be taken into account in the addition of the inputs at the model timestep. We recommend scaling the inputs to the seasonality of the freshwater inputs.</li> <li>The inputs from the riverine files should be added to the corresponding pool based on the following table:</li> <li> <table> <tbody> <tr> <td> <p>River Input</p> <p>(as named in <a href="../api/records/13684982/draft/files/rivr2o_riverinputs_preindustrial.nc/content" target="_blank" rel="noopener noreferrer">rivr2o_riverinputs_preindustrial.nc</a>)</p> </td> <td> <p>Global Load (preindustrial)</p> </td> <td> <p>Global Load </p> <p>(2011-2020 Mean)</p> </td> <td> <p>Ocean Model Pool</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>DIC -></p> </td> <td> <p>0.51 Pg C yr-1</p> </td> <td> <p>0.53 Pg C yr-1</p> </td> <td> <p>DIC & Alkalinity (see protocol)</p> </td> </tr> <tr> <td> <p>DOC_l -></p> </td> <td> <p>0.19 Pg C yr-1</p> </td> <td> <p>0.21 Pg C yr-1</p> </td> <td> <p>DIC</p> </td> </tr> <tr> <td> <p>DOC_sl -></p> </td> <td> <p>0.16 Pg C yr-1</p> </td> <td> <p>0.09 Pg C yr-1</p> </td> <td> <p>DOC_sl (new ocean model pool) and associated DON and DOP</p> </td> </tr> <tr> <td> <p>POC -></p> </td> <td> <p>0.095 Pg C yr-1</p> </td> <td> <p>0.12 Pg C yr-1</p> </td> <td> <p>marine DOC and associated nutrients (DON, DOP, see protocol)</p> </td> </tr> <tr> <td> <p>DIP -></p> </td> <td> <p>2.47 Tg P yr-1</p> </td> <td> <p>4.92 Tg P yr-1</p> </td> <td> <p>DIP / Phosphate</p> </td> </tr> <tr> <td> <p>DIN -></p> </td> <td> <p>20 Tg N yr-1</p> </td> <td> <p>30.03 Tg N yr-1</p> </td> <td> <p>DIN / Nitrate</p> </td> </tr> </tbody> </table> </li> </ul> <h2> </h2> <h2><strong>3. References</strong></h2> <p>Beusen, A. H. W., L. P. H. Van Beek, A. F. Bouwman, J. M. Mogollón, and J. J. Middelburg. Coupling Global Models for Hydrology and Nutrient Loading to Simulate Nitrogen and Phosphorus Retention in Surface Water-description of IMAGE–GNM and Analysis of Performance. Geoscientific Model Development, 8, no. 12 (2015): 4045–67. <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5194%2Fgmd-8-4045-2015&data=05%7C02%7CPierre.Regnier%40ulb.be%7Cbc3e3fa0c09249529ae508dccffc9a65%7C30a5145e75bd4212bb028ff9c0ea4ae9%7C0%7C0%7C638613931025683992%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=%2FAYnNpSwHioFv4igr0eRpwUW8HuFBDkY%2B9OmS8NpYZU%3D&reserved=0%22%20\o%20%22URL%20d%E2%80%99origine%C2%A0:%20https://doi.org/10.5194/gmd-8-4045-2015%20%20Cliquez%20pour%20suivre%20le%20lien." target="_blank" rel="noreferrer noopener">https://doi.org/10.5194/gmd-8-4045-2015</a>. </p> <p>Beusen, A. H. W., Bouwman, A. F., Van Beek, L. P. H., Mogollón, J. M., and Middelburg, J. J.: Global riverine N and P transport to ocean increased during the 20th century despite increased retention along the aquatic continuum, Biogeosciences, 13, 2441–2451, https://doi.org/10.5194/bg-13-2441-2016, 2016.</p> <p>Hansell, D. A., C. A. Carlson, and R. Schlitzer (2012), Net removal of major marine dissolved organic carbon fractions in the subsurface ocean, <em>Global Biogeochem. Cycles</em>, 26, GB1016, doi:<a title="Link to external resource: 10.1029/2011GB004069" href="https://doi.org/10.1029/2011GB004069" target="_blank" rel="noopener">10.1029/2011GB004069</a>.</p> <p>Lacroix, F., Ilyina, T., and Hartmann, J.: Oceanic CO<sub>2</sub> outgassing and biological production hotspots induced by pre-industrial river loads of nutrients and carbon in a global modeling approach, Biogeosciences, 17, 55–88, https://doi.org/10.5194/bg-17-55-2020, 2020.</p> <p>Liu et al. (2024). Global riverine land-to-ocean carbon export constrained by observations and multi-model assessment, Nature Geoscience, <a href="https://www.nature.com/articles/s41561-024-01524-z" target="_blank" rel="noopener">https://www.nature.com/articles/s41561-024-01524-z</a></p> <p>Ma, M., Zhang, H., Lauerwald, R., Ciais, P., and Regnier, P.: Estimating lateral nitrogen transfer through the global river network using a land surface model, Earth Syst. Dynam. Discuss. [preprint], <a href="https://doi.org/10.5194/esd-2024-29" target="_blank" rel="noopener">https://doi.org/10.5194/esd-2024-29</a>, in review, 2024.</p> <p>Regnier, P., Resplandy, L., Najjar, R.G. <em>et al.</em> The land-to-ocean loops of the global carbon cycle. <em>Nature</em> <strong>603</strong>, 401–410 (2022). https://doi.org/10.1038/s41586-021-04339-9</p> <p>Tian, H., Yao, Y., Li, Y., Shi, H., Pan, S., Najjar, R. G., et al. (2023). Increased terrestrial carbon export and CO<sub>2</sub> evasion from global inland waters since the preindustrial era. <em>Global Biogeochemical Cycles</em>, 37, e2023GB007776. <a href="https://doi.org/10.1029/2023GB007776">https://doi.org/10.1029/2023GB007776</a></p> <p>Yang, Qichun, Hanqin Tian, Marjorie A. M. Friedrichs, Charles S. Hopkinson, Chaoqun Lu, and Raymond G. Najjar.: Increased Nitrogen Export from Eastern North America to the Atlantic Ocean Due to Climatic and Anthropogenic Changes during 1901–2008. Biogeosciences,120, no. 6 (2015): 1046–68. <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1002%2F2014JG002763&data=05%7C02%7CPierre.Regnier%40ulb.be%7Cbc3e3fa0c09249529ae508dccffc9a65%7C30a5145e75bd4212bb028ff9c0ea4ae9%7C0%7C0%7C638613931025698138%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=cWTUjbft9Qkg5NOABA0WvTEQ9%2B8kl9GP78JOQJ9K074%3D&reserved=0%22%20\o%20%22URL%20d%E2%80%99origine%C2%A0:%20https://doi.org/10.1002/2014JG002763%20%20Cliquez%20pour%20suivre%20le%20lien." target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/2014JG002763</a>. </p> <p> </p> <h2><strong>4. Version Log</strong></h2> <p>v1 -> pre-industrial river inputs with coastal vegetation and burial transformations</p> <p>v2 -> Groundwater DIC discharge was added.</p> <p>v3-> Bugfixes for groundwater discharge and blue carbon inputs.</p> <p>v4 -> Corrected index with list <strong>riverexports_list_CN.csv </strong>for DIN inputs</p> <p>v5 -> corrected tDOC splits according to R2O-MIP protocol</p> <p>v8 -> Added submerged coastal vegetation fluxes to tDOC_semilabile</p> <p>v9 -> labile DON and labile DOP are added to the DIP and DON pools (based on C:N:P ratio of 2583:106:1)</p> <p>v10 -> slight correction in the labile DOM C:N:P ratio (C:N:P = 2583:103:1)</p> <p>v11 -> correction of labile DOM C:N:P ratio in list files</p> <p>v12 -> Addition of anthropogenic time series for 1901-2024</p> <p>v13 -> Corrected unit mistake in historical timeseries for DIN (10^3 magnitude too large)</p>
Dataset from "The Land-to-Ocean Loops of the Global Carbon Cycle" article
<p>Dataset of Tables S1, S2 and S3 from the paper entitled "The Land-to-Ocean Loops of the Global Carbon Cycle".</p>
Phanerozoic global climatic fields simulated using the FOAM ocean-atmosphere general circulation model
<p>These files contain the output of Phanerozoic global climate simulations conducted using the coupled ocean-atmosphere FOAM general circulation model. They are available every 20 Myrs between 540 Ma and 0 Ma, both included. All simulations have been conducted using identical boundary conditions; pCO2: 2240 ppm, solar luminosity: 1368 W m-2, vegetation: rocky desert, orbital configuration: null eccentricity and minimum obliquity. Only the continental configuration was varied from one time slice to the other (sensitivity test to the continental configuration), using the reconstructions of Scotese and Wright (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/).</p> <p>The reader is referred to the associated paper for a full description of the model and boundary conditions.</p> <p>All file names use the following pattern: "[age]rd_1368W_EccN_[model_component]_2240ppm.nc", with [age], the age expressed in million years ago, and [model_component] being 'atmos', 'ocean' or 'coupl' (atmospheric and oceanic components, plus coupler).</p>
NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model"
<p>NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model", submitted to Journal of Advances in Modelling the Earth System.</p> <p>The data are produced from an ensemble of six experiments based on the GO8p0 configuration of NEMO v4.0.1 on a global 1/4° grid, as described in the paper. The ensemble is intended to test the z~ vertical coordinate, and includes a control with the default "z-star" fixed coordinate, and five experiments with the z-tilde vertical coordinate, using a selection of values for the two z-tilde timescale parameters. The data includes time series of global mean ocean and ice fields; large-scale transports; and fields from diapycnal mixing analysis.</p> <p>The first part of each filename refers to the experiment from the ensemble ("zstar", "ztilde_5_30", "ztilde_10_30", "ztilde_20_30", ztilde_20_60" and "ztilde_40_60"); the following five-character string identifies the respective suite on the Met Office Rose system and the MASS archive system; and the rest of the name specifies the type of data contained in the file.</p>
Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity - ACCESS-OM2 data and plotting routines
<p>This repository contains the processed data and plotting routines associated with the article</p> <p>Holmes, Groeskamp, Stewart and McDougall (2022), Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity, Journal of Advances in Modeling Earth Systems (JAMES), doi: 10.1029/2021MS002914, http://dx.doi.org/10.1029/2021MS002914</p> <p>The contents includes post-processed data output from the 1-degree ACCESS-OM2 ocean-sea-ice model simulations and the python/jupyter plotting routines required to make the plots.</p> <p>The processing script is Holmes2022JAMES_Neutral_Diffusion_ACCESS-OM2_Plotting_Script.ipynb. The data files consist of time-averages or time series of certain metrics processed using NCO tools from the raw ACCESS-OM2 simulation output.</p>
Global characterization of the ocean's internal gravity wave vertical wavenumber spectrum from Argo float profiles
<p>Oceanic internal gravity wave energy levels E (m^2/s^2), vertical wavenumber spectral slopes s, and vertical wavenumber scale m* (1/m) estimated by fitting the Garrett Munk model vertical wavenumber shape function to strain spectra obtained from Argo float hydrographic profiles based on the finestructure method, as discussed in Pollmann (2020): "Global Characterization of the Ocean’s Internal Wave Spectrum" (<em>Journal of Physical Oceanography</em> 50.7: 1871-1891). The paper and hence this dataset are a contribution to the Collaborative Research Centre TRR181 ‘Energy Transfers in Atmosphere and Ocean’ funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Projektnummer 274762653. The hydrographic profiles used in this study were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System.</p> <p>Please cite Pollmann (2020) when using this dataset.</p> <p>This dataset includes:</p> <p>a) energy density (m^2/s^2) binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>b) vertical wavenumber spectral slopes binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>c) vertical wavenumber scale m* (1/m) binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>d) latitude and longitude, defined such that, e.g., E(10,10) represents energy levels in the bin bounded by lat(10), lat(11) as well as lon(10), lon(11)</p>
ScienceDex guides
Understand access before you commit
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.