Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
53
datasets available to search
ShareScore release 0.9.0
Dataset results
53 results for “Biogeochemical Modeling”
Data used in "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean"
<div> <p>This repository contains the data used to generate the figures for the submitted manuscript "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean".</p> </div> <h3>Contents</h3> <div> <ul> <li> <p>Model input:</p> <ul> <li> <p>INPUTS: ocean model input/grid files</p> </li> <li> <p>PISCES_INPUTS: BGC input files</p> </li> <li> <p>OBC: open boundary forcing </p> </li> <li> <p>WEIGHTS: weight files for ERA interim forcing</p> </li> </ul> </li> </ul> </div> <div> <ul> <li> <p>Manuscript files:</p> <ul> <li> <p>data: files used to generate manuscript images</p> </li> <li> <p>config, src, notebooks: Python code and Jupyter notebooks used to generate images</p> </li> <li> <p>figures, supplementary: manuscript figures and supplementary figures</p> </li> </ul> </li> </ul> </div> <div> </div> <div><strong>Abstract: </strong>"We present BIOPERIANT12, a regional model configuration of the Southern Ocean (SO) at a mesoscale-resolving 1/12 degree. This is a stable, ocean–ice–biogeochemical configuration derived from the Nucleus for European Modelling of the Ocean (NEMO) modelling platform. It is specifically designed to investigate questions related to the mean state, seasonal cycle variability and mesoscale processes in the mixed layer and within the upper ocean (<1000 m). In particular, the focus is on understanding processes behind carbon and heat exchange, systematic errors in biogeochemistry and assumptions underlying the parameters chosen to represent these SO processes. The dynamics of the ocean model play a large role in driving ocean biogeochemistry and we show that over the chosen period of analysis 2000–2009 that the simulated dynamics in the upper ocean provide a stable mean state, as compared to observation-based datasets (themselves subject to biases such as sparsity of data, cloud cover, etc.), and through which the characteristics of variability can be described. Using ocean biomes to delineate the major regions of the SO, the model demonstrates a useful representation of ocean biogeochemistry and partial pressure of carbon dioxide (pCO2). In addition to a reasonable model mean state performance, through model–data metrics BIOPERIANT12 highlights several pathways for improving Southern Ocean model simulations such as the representation of temporal variability and the overestimation of biological biomass."</div>
Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products
<p><strong>v2 update: </strong></p> <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> <p><br>The v2 data is used for the final published version of the Global Carbon Budget 2024.</p> <p>-----------------</p> <p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).</strong><br>There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: global, north, tropics, south<br>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br>area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude</p> <p>(3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. Temporal resolution: annual.</p> <p><strong>Fair data use statement:</strong><br>The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br><strong>Citation:</strong> Please cite the Global Carbon Budget 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2024 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).</p> <p><strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."<br><strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Cascading effects augment the direct impact of CO2 on phytoplankton growth in a biogeochemical model, links to model results
<p>This dataset provides the output of eight model simulations with the global ocean biogeochemical model FESOM-REcoM necessary to reproduce the findings of Seifert et al. (2022). In addition to information on the mesh, the dataset contains 1) 5-year means of global phytoplankton biomass, chlorophyll, net primary production, growth rates, limitations, calcification, grazing rates, calcite concentrations, zooplankton biomass, export fluxes as well as CO<sub>2(aq)</sub>, HCO<sub>3</sub><sup>-</sup> and nutrient concentrations, and 2) a time series of global and North Atlantic coccolithophore biomass, temperature, and CO<sub>2(aq)</sub> concentrations from 1958 to 2018.</p> <p>File names refer to the Figures and Tables in the paper where the respective data are used. See “readme” for detailed information on the dataset and separate files.</p>
Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"
<p>ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
Modelled isoscape data for: "Oceanographic and biogeochemical drivers cause divergent trends in the nitrogen isoscape in a changing Arctic Ocean"
<p>The data included in this repository includes the biogeochemical model output of nitrogen isotope fields. These data were generated by simulations with the NEMOv4.0 Ocean General Circulation Model, SI3 sea ice model, and Pelagic Interactions Scheme for Carbon and Ecosystem Studies version 2 (PISCESv2) biogeochemical model. Nitrogen isotopes were integrated within PISCESv2 for the purpoes of this study.</p> <p>All data here are in longitude, latitude and time cordinates. No depth coordinate is provided as all values are averaged over the upper 100 metres of the model.</p> <p> </p> <p>The file names mean the following:<br> </p> <p>ETOPO - refers to how the curvilinear, native grid of the model was re-gridded to a regular 360x180 longitude-latitude grid uisng the etopo60 coordinate system.</p> <p>JRA55 - these are the reanalysis-driven simulations, for which we used the Japanese Atmospheric Reanalysis (JRA55do).</p> <p>future - these are the emissions-driven simulations (historical from 1850-2005, then according to Representative Concentration Pathway 8.5 from 2006-2100.)</p> <p>picontrol - these are parallel to the emissions-driven simulations but do not include the increase in emissions.</p> <p>ndep - refers to if the historical increase in anthropogenic nitrogen deposition was included in the simulation</p> <p>d15Nno3 - isotopic composition of nitrate averaged over the upper 100 metres</p> <p>d15Npom - isotopic composition of particulate organic matter averaged over the upper 100 metres</p> <p>predictors - the average values of salinity, N* and particulate organic matter over the upper 100 metres</p> <p>annualave - annual averages, so that the data are inter-annual</p> <p>1970-1990ave_months - average monthy values over the period 1970-1990.</p>
FESOM-REcoM model data: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario
<p>This data set includes the minimal data necessary to reproduce the findings of Nissen et al. (2023). Output of model simulations with the global ocean biogeochemical model FESOM1.4-REcoM2 is provided. In particular, besides information on the model grid, the data set includes annual mean water mass properties (temperature, salinity, density, oxygen, pH) and freshwater fluxes from sea ice and ice shelves and decadal averages of air-sea CO2 fluxes and deep-ocean carbon accumulation rates. Model results are provided from 1980-2100 for the four emission scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (sorted from low emission to high emission).</p> <p>Please see README for more information on the individual files. </p> <p>Data set belongs to: </p> <p>Nissen, C., R. Timmermann, M. Hoppema, and J. Hauck, 2023: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario. <em>J. Climate</em>, <a href="https://doi.org/10.1175/JCLI-D-22-0926.1">https://doi.org/10.1175/JCLI-D-22-0926.1</a>, in press.</p>
Modeling biogeochemical responses of tundra ecosystems to temporal and spatial variations in climate in the Kuparuk River Basin , Alaska, 1921 to 2100.
Output data set of the MBL-GEM III model run for tussock tundra in the Kuparuk River Basin, Alaska, described in detail in Le Dizès, S., B. L. Kwiatkowski, E. B. Rastetter, A. Hope, J. E. Hobbie, D. Stow, and S. Daeschner, Modeling biogeochemical responses of tundra ecosystems to temporal and spatial variations in climate in the Kuparuk River Basin (Alaska), J. Geophys. Res., 108(D2), 8165, doi:10.1029/2001JD000960, 2003. We ran the model at a 10 km x 10 km resolution for 123 cells at a yearly time step for 180 years, from 1921 to 2100. Two scenarios enabled the investigation of the effects of two opposing climate change scenarios for the 2001-2100 future period: warmer and wetter ("wet scenario" or Scenario 1) and warmer and drier ("dry scenario" or Scenario 2). These 246 files contain all simulation results for each scenario for individual cells in the Kuparuk River basin.
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>
In situ dataset for initialization and validation of the Copernicus Med-MFC biogeochemical model system (MedBGCins)
<p>The biogeochemical model system in use by the Mediterranean Monitoring Forecasting Centre (Med-MFC) of the EU Copernicus Marine Service requires several observational datasets for data assimilation and model initialization and validation (Coppini et al., 2023; Cossarini et al., 2021; Salon et al., 2019). The present MedBGCins dataset consists of the in situ measurements, coming from selected platforms, on which the initialization and validation of the biogeochemical model system are built. The MedBGCins dataset collects in situ measurements along the Mediterranean Sea water column and during the 1995-2023 time period for nutrients (i.e., nitrate, nitrite, phosphate, silicate, ammonium), dissolved oxygen, dissolved inorganic carbon, total alkalinity, total scale pH at 25°C. The dataset also provides pCO2 and total scale pH at in situ conditions, reconstructed by using the PyCO2SYS Python toolbox (Humpreys et al., 2024). The complete list of variables is indicated in Table 1. The largest subset of the original data are from EMODnet Chemistry Mediterranean Sea - Eutrophication and Acidity aggregated datasets 1911/2022 v2023 (reference in Table 2), including both profiles and time series, plus other documented cruises (same table).</p> <p>Additional information and references are included in the UserGuide file.</p> <p> </p>
Fig. 2 in The Challenges of Incorporating Realistic Simulations of Marine Protists in Biogeochemically Based Mathematical Models
Fig. 2. The mechanistic phytoplankton model of Flynn (2001) that represents multi nutrient uptake and utilisation of N – nitrate; A – ammonium; F – bioavailable iron; P – phosphate; S – silicate; and the interaction with light (PFD). Major flows in and out of state variables (boxes) are depicted by solid arrows, with the major feedback processes depicted by dashed arrows. C – carbon biomass; Cell – cell density; NC – N C-quota; ChlC – chlorophyll C-quota, FC – iron C-quota; IPC – inorganic P C-quota, OPC – organic P C-quota; Scell – silicon cell-quota (reproduced with permission).
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 and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"
<p>Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript.</p> <p>The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review.</p> <p>Please consult the README file for information on the specifications of the files.</p> <p>These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.</p>
A Pelagic Size Structure database (PSSdb) to support biogeochemical modeling: third update to first release of PSSdb-bulk
<p>This dataset represents the third update to the first release of the Pelagic Size Structure database (PSSdb, <a href="https://pssdb.net">https://pssdb.net</a>) scientific project, investigating the global particle size distributions measured from multiple pelagicǂ imaging systems. These devices include the Imaging Flow Cytobot (Olson and Sosik 2007), benchtop scanners like the ZooScan (Gorsky et al. 2010), and the Underwater Vision Profiler (Picheral et al. 2010). The data sources originate from Ecotaxa (<a href="https://ecotaxa.obs-vlfr.fr/">https://ecotaxa.obs-vlfr.fr/</a>), Ecopart (<a href="https://ecopart.obs-vlfr.fr/">https://ecopart.obs-vlfr.fr/</a>), and Imaging FlowCytobot dashboards (<a href="https://ifcb.caloos.org/dashboard">https://ifcb.caloos.org/dashboard</a> and <a href="https://ifcb-data.whoi.edu/dashboard">https://ifcb-data.whoi.edu/dashboard</a>). Links to the PSSdb code and documentation are available on the PSSdb webpage (<a href="https://pssdb.net">https://pssdb.net</a>). </p> <p>This <em>updated version</em> includes the following changes: </p> <p>● Duplicate data entries and NaN values have been removed.<br>● Data products now include Normalized Biomass Size Spectra (NBSS), and Particle Size Distribution (PSD), two widely used methods to represent plankton and particles size distribution in marine ecology and biogeochemistry (Jonasz and Fournier 1996, San Martin et al. 2006).<br>● Linear regressions are now performed with log10 transformations of the normalized biovolume/abundance and the size classes.<br>● Inclusion of UVP6 and other benchtop plankton Scanner datasets from net tows, which expand the temporal and spatial coverage of the data products.<br>● Unbiased portion of the size spectra is selected by a new thresholding method that accounts for both uncertainties on particle sizes, limited by the camera resolution, and particle count (Schartau et al. 2010), so that only size classes with less than 20% uncertainty are retained, in addition to gaps in the size spectra.</p> <p>Added in this version (March, 2024):<br>● An error in the thresholding function (scripts/funcs_NBS.py) was corrected.<br>● A quality control function was implemented (scripts/funcs_NBS.py) to flag size spectra calculations in products 1a and 1b.</p> <p>Added in this version (April, 2024):</p> <p><span><span>●<span> </span></span></span><span>An error in the size classes defined in the <a href="https://github.com/jessluo/PSSdb/blob/main/ancillary/ecopart_size_bins.tsv"><span>ecopart_size_bins.tsv</span></a> used by the size binning function (<a href="https://github.com/jessluo/PSSdb/commit/9e3c52179b2d3333971028a4a023a06e48444283#diff-28c0193fe5dad62dccba0020363d6cc496a8921fccb723a5e4bf3608d3dd0879"><span>scripts/funcs_NBS.py</span></a>) was corrected, the size ratio between consecutive size bins is the same across all the size ranges now.</span></p> <p> </p> <p>This PSSdb dataset is composed of two products, specific to each imaging device: </p> <ul> <li><strong>Product </strong>1a includes the size distribution , computed from normalized biovolume, for NBSS, and normalized abundance, for PSD, of plankton and particles within a set of pre-defined size classes (expressed in both biovolume and equivalent circular diameter), averaged by year and month, and in 1-degree longitude/latitude grid cells.</li> <li><strong>Product 1b</strong> includes the results of NBSS and PSD regression fit parameters, slopes, intercept, and coefficient of determination (R2), averaged by year and month, and in 1-degree longitude/latitude grid cells. The regression parameters are defined using ordinary least squares linear regressions applied to a log10 transformed normalized biovolume/normalized abundance and biovolume/ diameter size class values.</li> </ul> <p>Size spectra parameters were averaged over a maximum of 16 spatial and temporal subsets (0.5°x0.5°x1 week) to avoid over-representation of repeated sampling events (e.g., time-series datasets) within a grid cell. Linear regressions were performed on the linear portion of the log10-transformed NBSS and PSD estimates, between the size classes with a size measurement or particle count uncertainty greater than 20% (Schartau et al. 2010) , and where the maximum NB/PSD is observed and the largest size class before three empty consecutive size classes.</p> <p><em> <strong> For additional information, please see the PDF documentation available below ...</strong></em></p> <p> </p>
PSSdb-Taxa: A Pelagic Size Structure database taxa-specific product to support biogeochemical modeling: Update to first release of taxa-specific products
<p>This dataset represents the first update to the first release of taxa-specific products from the Pelagic Size Structure database (PSSdb,<a href="https://pssdb.net/"> </a><a href="https://pssdb.net">https://pssdb.net</a>), a scientific project investigating the global particle size distributions measured from multiple pelagic imaging systems. These devices include the Imaging Flow Cytobot (Olson and Sosik 2007), benchtop scanners like the ZooScan (Gorsky et al. 2010), and the Underwater Vision Profiler (Picheral et al. 2010). The data sources originate from Ecotaxa (<a href="https://ecotaxa.obs-vlfr.fr/">https://ecotaxa.obs-vlfr.fr/</a>), Ecopart (<a href="https://ecopart.obs-vlfr.fr/">https://ecopart.obs-vlfr.fr/</a>), and Imaging FlowCytobot dashboards (<a href="https://ifcb.caloos.org/dashboard">https://ifcb.caloos.org/dashboard</a> and<a href="https://ifcb-data.whoi.edu/dashboard"> </a><a href="https://ifcb-data.whoi.edu/dashboard">https://ifcb-data.whoi.edu/dashboard</a>).</p> <p>Taxa-specific products were generated after standardization of the automated or manual taxonomic annotations assigned to individual particles following the recent guidelines of Neeley et al. (2021). We used the World Register of Marine Species (WoRMS, <a href="https://www.marinespecies.org/">https://www.marinespecies.org/</a>) to assign each particle its final taxonomic annotation, and published group-specific relationships linking biovolume to carbon biomass or dry weight.</p> <p>The herein taxa-specific products include both taxonomic class-specific data, obtained after grouping all particles in a given taxonomic class<strong><sup>§</sup></strong>, and broad plankton functional type (PFT) data<strong><sup>Ɨ</sup></strong>. Detrital materials were also separated based on common categories (marine snow, aggregate, fecal pellet) and known biovolume-to-biomass conversion factors (Durkin et al. 2021). Links to the PSSdb code (including the taxonomic and allometric look-up tables) and documentation are available on the PSSdb webpage (<a href="https://pssdb.net">https://pssdb.net</a>).</p> <p> </p> <p>This <em>updated version</em> includes the following changes (modified in April, 2024):</p> <p>● An error in the size classes defined in the <a href="https://github.com/jessluo/PSSdb/blob/main/ancillary/ecopart_size_bins.tsv">ecopart_size_bins.tsv</a> used by the script generating taxa-specific products (<a href="https://github.com/jessluo/PSSdb/commit/b5725df2f32f0300ab64b0a630136c7f5a6857c7">scripts/5_compute_taxa_NBSS.py</a>) was corrected, the size ratio between consecutive size bins is the same across all the size range.</p> <p> </p> <p><em><strong> For additional information, please see the PDF documentation available below ...</strong></em></p>
PSSdb-Merged: A Pelagic Size Structure database merged product to support biogeochemical modeling: First release of Merged products
<p>This dataset represents the first release of <strong>merged products</strong> from the Pelagic Size Structure database (PSSdb,<a href="https://pssdb.net/"> </a><a href="https://pssdb.net/">https://pssdb.net</a>), a scientific project investigating the global particle size distributions measured from multiple pelagic imaging systems. These devices include the Imaging Flow Cytobot (Olson and Sosik 2007), benchtop scanners like the ZooScan (Gorsky et al. 2010), and the Underwater Vision Profiler (Picheral et al. 2010). The data sources originate from Ecotaxa (<a href="https://ecotaxa.obs-vlfr.fr/">https://ecotaxa.obs-vlfr.fr/</a>), Ecopart (<a href="https://ecopart.obs-vlfr.fr/">https://ecopart.obs-vlfr.fr/</a>), and Imaging FlowCytobot dashboards (<a href="https://ifcb.caloos.org/dashboard">https://ifcb.caloos.org/dashboard</a> and<a href="https://ifcb-data.whoi.edu/dashboard"> </a><a href="https://ifcb-data.whoi.edu/dashboard">https://ifcb-data.whoi.edu/dashboard</a>).</p> <p><em><strong>Merged products</strong></em> correspond to the most complete datasets in PSSdb, whereby all instrument-specific datasets are merged together to produce a single, quasi-continuous size spectrum where only optimal values in overlapping size classes are selected for each spatio-temporal bin. The resulting spectra are produced after generating the taxa-specific products following the recent approach of Soviadan et al. (2023). Briefly, this approach assumes that one device is best suited in detecting certain taxa (amongst the living functional groups, see documentation), and size classes (depending on the resolution of the camera). As a result, we consider the maximum observed normalized biovolume/biomass and abundance amongst a set of non-null values from different instruments as the optimal measure for detection.</p> <p>Links to the PSSdb code (including the taxonomic and allometric look-up tables) and documentation are available on the PSSdb webpage (<a href="https://pssdb.net/">https://pssdb.net</a>).</p> <p> </p> <p><em><strong> For additional information, please see the PDF documentation available below ...</strong></em></p>
Code and data archive to accompany "A derivative-free optimisation method for global ocean biogeochemical models", Oliver et. al. 2021
<p>This archive is to accompany the article:</p> <p>A derivative-free optimisation method for global ocean biogeochemical models,<br> Sophy Oliver, Coralia Cartis, Iris Kriest, Simon Tett, and Samar Khatiwala.</p> <p>The optimisation framework used in this study can be found here: https://doi.org/10.5281/zenodo.5517610</p> <p>The original source code of MOPS were from the Supplement of Kriest et al. (2017).<br> The most recent TMM source code is available at https://github.com/samarkhatiwala/tmm.</p> <p>In this archive:</p> <p>Supplement/Configurations/OxfordMOPS_Configs contains:<br> - ReadOnlyFiles (Files and Code specifically used to run the global ocean biogeochemical model MOPS model with<br> the Transport Matrix Method, which have been edited to differ from the versions downloaded from the sources above.)<br> - RunCode (runscripts to run the MOPS model with the TMM)<br> - TWIN_Configs (JSON files required by each optimisation experiment carried out).</p> <p>Supplement/OxfordMOPS_EXP contains data for each iteration of all optimisation experiments carried out.</p> <p>Supplement/OPTCLIMSO_PlottingScripts contains MATLAB plotting scripts used to create results figures of these experiments.</p>
A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC)
<p>A boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from black spruce (Picea mariana)-dominated mature forest stands.</p> <p>Dataset glossary and users’ instructions are documented in ‘README.md’. </p>
A 1D coupled physical-biogeochemical model for the North Atlantic for studying vertical carbon flux parameterizations
<p>This repository provides the model output and code for analysis in the following article:</p> <p>Wang, B., & Fennel, K. (2023). An assessment of vertical carbon flux parameterizations using backscatter data from BGC Argo. <em>Geophysical Research Letters</em>, 50, e2022GL101220. <a href="https://doi.org/10.1029/2022GL101220">https://doi.org/10.1029/2022GL101220</a></p> <p> </p>
Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle"
<p>Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle". </p> <p>Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022.</p> <p>Three directories can be downloaded:</p> <p><strong>DataOBS</strong> : AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022). </p> <p><strong>FigPaper </strong>: Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282). </p> <p><strong>MY_SRC_PISCES_NEMO_3.6 :</strong> Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)</p>
Data for A multi-phase biogeochemical model for mitigating earthquake-induced liquefaction via microbially induced desaturation and calcium carbonate precipitation
<p>This data accompanies the paper published in Biogeosciences, which can be found at https://doi.org/10.5194/egusphere-2022-1419.</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.