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53 results for “Biogeochemical Modeling”
Variable Stoichiometry Effects on Glacial/Interglacial Ocean Model Biogeochemical Cycles and Carbon Storage - MODEL OUTPUT
<p>This is the repository for the model output and controls pertaining to the simulation experiments performed in "Variable Stoichiometry Effects on Glacial/Interglacial Ocean Model Biogeochemical Cycles and Carbon Storage" by Nathaniel Fillman, Andreas Schmittner, and Karin Kvale. Citation and DOI for parent publication will be updated here when available.<br> See https://github.com/fillmann/variable-stoichiometry for model code.</p>
Carbon export estimated from an inverse biogeochemical model (Wang et al.,)
<p>Carbon export estimated from an inverse biogeochemical model (Wang et al.,)</p> <p>The four files are from two NPP configurations: CbPM and CAFE and two time scales for labile DOC remineralization e-folding time (12 hours and 24 hours), which are self-explained in the file name.</p> <p>Once the data has been loaded to Matlab, you'll see a structure EXP that contains a total of 9 fields: POCexp: </p> <p>POCexp, lDOCexp, sDOCexp are POC, labile DOC, semi-labile DOC flux at the base of model euphotic zone (~73 m).</p> <p>POC100, lDOC100, sDOC100 are POC, labile DOC, semi-labile DOC flux at 100 m.</p> <p>TOCmld is total carbon export at the base of maximum mixed layer depth.</p> <p>TOC110 and POC1000 are total carbon export at 110 and POC flux at 1000 m, respectively. </p> <p>The units for the export fluxes are in mmol m-2 year-1, and not in mg C m-2 day-1 as used in the paper. For conversion, you need to divide the value by 365.25 and multiply 12.</p>
Supplemental data for "Initial land use/cover distribution substantially affects global carbon and local temperature projections in the integrated Earth System Model." Article published as Global Biogeochemical Cycles publication 2019B006383
<p>These are supporting data for Global Biogeochemical Cycles publication 2019B006383: "Initial land use/cover distribution substantially affects global carbon and local temperature projections in the integrated Earth System Model." They include data for all of the regular and supplemental figures.</p>
NASA Ocean Biogeochemical Model assimilating satellite chlorophyll data global daily VR2017 (NOBM_DAY) at GES DISC
This is the assimilated daily data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth > 200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC).
CMS: Simulated Physical-Biogeochemical Data, SABGOM Model, Gulf of America, 2005-2010
This dataset contains monthly mean ocean surface physical and biogeochemical data for the Gulf of America simulated by the South Atlantic Bight and Gulf of America (SABGOM) model on a 5-km grid from 2005 to 2010. The simulated data include ocean surface salinity, temperature, dissolved inorganic nitrogen (DIN), dissolved inorganic carbon (DIC), partial pressure of CO2 (pCO2), air-sea CO2 flux, surface currents, and primary production. The SABGOM model is a coupled physical-biogeochemical model for studying circulation and biochemical cycling for the entire Gulf of America to achieve an improved understanding of marine ecosystem variations and their relations with three-dimensional ocean circulation in a gulf-wide context.
NASA Ocean Biogeochemical Model assimilating satellite chlorophyll data global monthly VR2017 (NOBM_MON) at GES DISC
This is the assimilated monthly data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth >200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC).
PnET-BGC: Modeling Biogeochemical Processes in a Northern Hardwood Forest Ecosystem
This archived model product contains the directions, executables, and procedures for running PnET-BGC to recreate the results of Gbondo-Tugbawa, S.S., C.T. Driscoll , J.D. Aber and G.E. Likens. 2001. The evaluation of an integrated biogeochemical model (PnET-BGC) at a northern hardwood forest ecosystem. Water Resources Research 37:1057-1070. Gbondo-Tugbawa et al,. 2001 Excerptfrom Abstract: An integrated biogeochemical model (PnET-BGC) was formulated to simulate chemical transformations of vegetation, soil, and drainage water in northern forest ecosystems. The model operates on a monthly time step and depicts the major biogeochemical processes, such as forest canopy element transformations, hydrology, soil organic matter dynamics, nitrogen cycling, geochemical weathering, and chemical equilibrium reactions involving solid and solution phases. The model was evaluated against soil and stream data at the Hubbard Brook Experimental Forest, New Hampshire. Model predictions of concentrations and fluxes of major elements generally agreed reasonably well with measured values, as estimated by normalized mean error and normalized mean absolute error. Model output of soil base saturation and stream acid neutralizing capacity were sensitive to parameter values of soil partial pressure of carbon dioxide, soil mass, soil cation exchange capacity, and soil selectivity coefficients of calcium and aluminum. PnET-BGC can be used as a tool to evaluate the response of soil and water chemistry of forest ecosystems to disturbances such as clear-cutting, climatic events, and atmospheric deposition.PnET-BGC, was used to investigate inputs and dynamics of S in a northern hardwood forest at the Hubbard Brook Experimental Forest (HBEF) (Gbondo-Tugbawa et al., 2002). The changes in soil S pools and stream-water were simulated to assess the response 22 SO4 to both atmospheric S deposition and forest clear-cutting disturbances. Watershed studies across the northeastern United States have shown that stream losses of exceed atmospheric sulfur (S) deposition. Understanding the processes responsible for this additional source of S is critical to quantifying ecosystem response to ongoing and potential future controls on SO2 emission.
Dynamic Ligand Impacts on Dissolved Iron Distributions in a Global Ocean Biogeochemical Model
<p>Annual mean CESM ocean output files on the coarse resolution gx3v7 grid.<br> Archived here in support of the paper submitted to JAMES by<br> Sherman et al. "Dynamic Ligand Impacts on Dissolved Iron Distributions in a<br> Global Ocean Biogeochemical Model".</p> <p> </p> <p>gdev.450 = dynamic ligand<br> gdev.457 = constant ligand = 1 nM<br> gdev.460 = implicit ligand</p> <p>gdev.458 = dynamic ligand + Sol Fe Depo X3<br> gdev.459 = constant ligand = 1 nM + Sol Fe Depo X3<br> gdev.461 = implicit ligand + Sol Fe Depo X3</p>
Model output for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"
<p>This dataset contains the numerical model output files used in the analysis described in "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"</p>
Climate model (CM2.6) and regional model (ACM) outputs used to investigate the physical drivers and biogeochemical effects of the weakening of the northwest North Atlantic Shelfbreak Jet (Garcia-Suarez & Fennel., 2024; JAMES)
<p>Key variables from the climate model GFDL CM2.6 and the regional Atlantic Canada model (ACM) used to investigate the physical drivers and the biogeochemical effects of the weakening of the shelfbreak jet in the northwest North Atlantic Ocean. The dataset includes all model variables required to reproduce the key results in <em>Garcia-Suarez & Fennel (2024, JAMES)</em>. See <em>GarciaSuarezandFennel_JAMES_CM26_ACM_data_README.txt</em> for more details.</p>
CMS: Simulated Physical-Biogeochemical Data, SABGOM Model, Gulf of Mexico, 2005-2010
This dataset contains monthly mean ocean surface physical and biogeochemical data for the Gulf of Mexico simulated by the South Atlantic Bight and Gulf of Mexico (SABGOM) model on a 5-km grid from 2005 to 2010. The simulated data include ocean surface salinity, temperature, dissolved inorganic nitrogen (DIN), dissolved inorganic carbon (DIC), partial pressure of CO2 (pCO2), air-sea CO2 flux, surface currents, and primary production. The SABGOM model is a coupled physical-biogeochemical model for studying circulation and biochemical cycling for the entire Gulf of Mexico to achieve an improved understanding of marine ecosystem variations and their relations with three-dimensional ocean circulation in a gulf-wide context.
model output used for Paper "Simulating ecosystem dynamics and marine biogeochemical cycles with multiple plankton functional types"
<p>This dataset contains the model output from CESM2.2-8p4z, used in Yu et al., 2024 in Journal of Advances in Modeling Earth Systems (JAMES). </p>
Example simulation showing spatial and temporal variations in surface carbon biomass of plankton functional groups during a Spring bloom as shown by a 3D hydrodynamic-biogeochemical model (FVCOM-ERSEM), with and without integration of the mixoplankton paradigm.
<p>The outputs are from simulations from using the FVCOM hydrodynamic model coupled to two different versions of ERSEM – (i) ERSEM and (ii) ERSEM-PB (the latter includes the implementation of the mixoplankton paradigm through integration of the 'Perfect Beast' PB model; Flynn and Mitra 2009 <em>Journal of Plankton Research</em>).</p> <p>The FVCOM domain was configured to represent Lyme Bay: a protected bay on the South Coast of England. This region is an important area for shellfish aquaculture. The domain was configured at 350 m – 5 km high-resolution, resolving sub-km scale dynamics in the area. A nested modelling approach of increasing model resolution was set up using two model domains. For the coupled hydrodynamic-biogeochemical model, a parent domain of 1.5 km – 10 km resolution was used to drive Lyme Bay model domain. The atmospheric forcing was provided by a 3-step downscaling of GFS global datasets to reach the 3 km of the final model domain using the Weather Research Forecast (WRF) model. Hydrodynamic boundary conditions are extracted from the European Copernicus Marine System North West European Shelf Forecast system. River flows were extracted from a National scale hydrology model run by the Center for Hydrology and Ecology in the UK. Simulations were initialised at Jan 1<sup>st</sup> 2005, and spun up for 3 months prior to the output of the data visualised in these videos. </p> <p>The 6 videos portray spatial and temporal variation of daily averaged surface carbon biomass (μgC L<sup>-1</sup>) during the month of April 2005 for the different plankton functional types (FTs) as follows:</p> <ul> <li>Video 1: all phytoplankton FTs in standard ERSEM grouped together. These thus include diatoms, nano-, pico- and micro- plankton; i.e., these simulations do not discriminate between phytoplankton and constitutive mixoplankton (CM).</li> <li>Video 2: phytoplankton FT in ERSEM-PB now considering only diatoms and picoplankton (i.e., cyanobacteria) only; CM are now included in Video 3 outputs.</li> <li>Video 3: all mixoplankton FTs grouped together in ERSEM-PB. These outputs thus include biomasses of micro-CM, nano-CM and NCM.</li> <li>Video 4: all zooplankton FTs grouped together in standard ERSEM. Thus, these include nanoflagellates, meso- and micro- zooplankton and thus includes the primary producing non-constitutive mixoplankton</li> <li>Video 5: zooplankton FT representing only the heterotrophic nano- and micro- zooplankton in ERSEM-PB.</li> <li>Video 6: spatio-temporal variability between the constitutive and non-constitutive mixoplankton functional groupings within FVCOM-ERSEM-PB. </li> </ul> <p>For further information about the mixoplankton paradigm, please see the following open access publications and references there in:</p> <p>Mitra A, Caron DA, Faure E, Flynn KJ, Leles SG, Hansen PJ, McManus GB, Not F, Gomes HR, Santoferrara L, Stoecker DK, Tillmann U (2023) <strong>The Mixoplankton Database – diversity of photo-phago-trophic plankton in form, function and distribution across the global ocean</strong>. <em>Journal of Eukaryotic Microbiology</em>, e12972. <a href="https://doi.org/10.1111/jeu.12972">https://doi.org/10.1111/jeu.12972</a></p> <p>Glibert PM, Mitra A (2022) <strong>From webs, loops, shunts, and pumps to microbial multitasking: evolving concepts of marine microbial ecology, the mixoplankton paradigm, and implications for a future ocean</strong>. <em>Limnology and Oceanography</em> 67: 585-597 <a href="https://doi.org.10.1002/lno.12018">https://doi.org.10.1002/lno.12018</a> </p> <p>Mitra A, Irigoien X (2022) <strong>Mixoplankton – Marine Organisms that break the rules</strong>. EU Researcher. <a href="https://issuu.com/euresearcher/docs/mixitin_eur28_h_res">https://issuu.com/euresearcher/docs/mixitin_eur28_h_res</a> </p> <p>Flynn KJ, Mitra A, Anestis K, Anschütz AA, Calbet A, et al. (2019) <strong>Mixotrophic protists and a new paradigm for marine ecology: where does plankton research go now?</strong> <em>Journal of Plankton Research</em> 41: 375-391 <a href="https://doi.org/10.1093/plankt/fbz026">https://doi.org/10.1093/plankt/fbz026</a></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.