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111 results for “Ocean productivity”
Size-Fractionated Chlorophyll a, Primary Productivity, and Photosynthetic Physiological Parameters of Phytoplankton in the Cosmonaut Sea, Southern Ocean, During Summer 2022
This dataset provides vertical distribution profiles of size-fractionated phytoplankton parameters measured in the Cosmonaut Sea, a marginal ice zone in the Southern Ocean, during the austral summer of 2022. Sampling was conducted across multiple stations spanning latitudes from approximately 33°N to 60°N and longitudes from -62°E to -67°E, focusing on surface and subsurface waters up to depths of about 40 meters. The data capture key aspects of phytoplankton physiology and productivity in this dynamic polar environment, influenced by seasonal ice melt and nutrient availability. Parameters include chlorophyll a concentrations (Chl a), primary productivity indicators such as maximum photosynthetic rates (PBm), photosynthetic efficiency (α), saturation irradiance (Ek), and integrated gross primary productivity (IGPPeu), all differentiated by size fractions: net phytoplankton (>20 μm), nano- and pico-phytoplankton (<20 μm), and total community. Additional measurements encompass photosynthetically active radiation (PAR) and mixed layer depths, providing context for light and stratification effects on phytoplankton dynamics. Data were derived from in situ incubations and fluorometric analyses, with values reported for discrete depths at each station to highlight vertical gradients in biomass and photosynthetic performance. This completed dataset is particularly valuable for studies on polar marine ecosystems, carbon cycling, and climate-driven changes in phytoplankton communities, offering insights into how size-structured assemblages respond to environmental gradients in the Southern Ocean. It does not include taxonomic details beyond general phytoplankton groupings but emphasizes physiological metrics for modeling primary production in ice-influenced regions.
Global Carbon Budget 2022, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual Global ocean biogechemical models and surface ocean fCO2-based data-products
<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 (data-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 data-products and GOBMs and with the adjustments described in the Global Carbon Budget 2022 (https://doi.org/10.5194/essd-14-4811-2022, section C3), are available in the Global Carbon Budget 2022 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 13 of the Global Carbon Budget 2022 paper (https://doi.org/10.5194/essd-14-4811-2022), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.17 GtC yr-1, Tropics: 0.16 GtC yr-1, South: 0.32 GtC yr-1, see GCB 2022 paper, section 2.4.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because 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):</p> <p><br> fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: 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><br> (3) One file ‘GCB-2022_OceanModel_RegionalBreakdown_1959-2021.nc’ with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p> <p><br> <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 2022 (Friedlingstein et al., 2022, ESSD, https://doi.org/10.5194/essd-14-4811-2022) 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 2022 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).<br> <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><br> Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 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://globalcarbonbudget.org/</p> <p> </p>
Data set: Can ocean community production and respiration be determined by measuring high-frequency oxygen profiles from autonomous floats?
<p>Relevant autonomous float data for <a href="https://doi.org/10.5194/bg-17-4119-2020">Gordon et al. (2020)</a>. Following a similar structure to the Argo network's "synthetic" profile files, one file per float is produced with all relevant variables (temperature, salinity, chlorophyll, backscatter, dissolved oxygen) on a common depth and time grid. The Electro-Magnetic Autonomous Profiling Explorer (EM-APEX) floats were deployed in the northern Gulf of Mexico in May 2017 - see <a href="https://doi.org/10.1109/CWTM43797.2019.8955168">Shay et al. (2019)</a> for more information. </p> <p>The data published here contains a timestamp for each data point. Another version of this data which contains some additional variables is hosted on <a href="https://data.gulfresearchinitiative.org/data/R5.x275.281:0001">GRIIDC</a>, but does not contain a timestamp for each data point, but rather for each profile. </p>
Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in 'winning' hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.
<p>Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in ‘winning’ hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.</p>
Global Carbon Budget 2023, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogechemical models and surface ocean fCO2-based data-products
<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 2023 (https://doi.org/10.5194/essd-15-5301-2023), are available in the Global Carbon Budget 2023 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 13 of the Global Carbon Budget 2023 paper (https://doi.org/10.5194/essd-15-5301-2023), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2023 paper, section 2.5.1). 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: 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<br><br>(3) One file 'GCB-2023_OceanModel_RegionalBreakdown_1959-2022.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. 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 2023 (Friedlingstein et al., 2023, ESSD, https://doi.org/10.5194/essd-15-5301-2023) 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 2023 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).<br><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>
Adjusted ERA5, COREv2 and JRA-55 products constrained by ocean observations
<p>This dataset contains ERA5, JRA-55 and COREv2 air-sea flux fields that have been adjusted to match ocean heat and salt content change in EN4 and IAP ocean observations. It also contains estimes of meridional heat and freshwater transports, globally and in the Atlantic and Indo-Pacific, based on these adjusted air-sea surface flux fields. Please consult the README for more information on the dataset. <br><br>The net heat flux and net freshwater flux into the ocean have been adjusted using the "Optimal Transformation Method" (OTM), a watermass-based inverse method that uses physics-based constraints to close the observed ocean heat and salt budgets. The formulation of OTM and a model validation is provided at Zika & Sohail (2024). The process of producing these adjusted air-sea fluxes is described in Sohail & Zika (2025).</p>
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>
Seawater isoprene loss and production rates across contrasting oceanic regions
<p><strong>Measured biological variables and isoprene process rate constants across contrasting regions of the global ocean. </strong>SST: sea surface temperature;<strong> </strong>SSS: sea surface salinity; Z<sub>ML</sub>: mixed layer depth; U<sub>10</sub> 24h: wind speed at 10 m above sea surface, averaged over 24 hours; chl<em>a</em>: chlorophyll-<em>a</em> concentration; BA: bacterial abundance; k<sub>loss</sub>: rate constant of isoprene loss in incubations (microbial degradation + chemical oxidation); k<sub>vent</sub>: rate constant of isoprene ventilation to the atmosphere; k<sub>mix</sub>: rate constant of isoprene vertical mixing by turbulent diffusion at the bottom of the mixed layer (negative means import into the surface mixed layer); total τ: turnover time due to all sinks; k<sub>prod</sub>: rate constant of isoprene production, assuming 24-h steady state for the isoprene concentration; sp. prod. rate: chl<em>a</em>-normalized daily rate of isoprene production.</p> <p><strong>Results of the coastal seawater dark incubations for isoprene loss kinetics. </strong>SST: lab-incubation temperature (within ±0.5ºC of the in-situ temperature); chl<em>a</em>: chlorophyll-<em>a</em> concentration; isoprene concentration; std err: standard error of duplicate isoprene concentration measurements. The experiments were conducted with water from the Blanes Bay Microbial Observatory (coastal NW Mediterranean) and the coral reef lagoon of Moorea (French Polynesia).</p> <p><strong>Results of the coastal seawater dark incubations for isoprene oxidation assays. </strong>SST: in-situ and incubation temperature; chl<em>a</em>] chlorophyll-<em>a</em> concentration; isoprene concentration. </p>
Processing and Data for "Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats"
<p><strong>Description: </strong></p> <p>These files contain processed BGC-Argo float data, figure data, the radiocarbon productivity subset, bootstrapping results, and the associated Python/Matlab code to calculate net primary productivity from daily cycles of optical backscatter and dissolved oxygen.</p> <p>The raw float data used in this study are available from the Argo Global Data Assembly Centers in Brest, France (ftp://ftp.ifremer.fr/ifremer/argo/dac/coriolis) and Monterey, California (ftp://usgodae.org/pub/outgoing/argo/dac/coriolis). The raw MODIS satellite-based productivity data is available from the Oregon State University Ocean Productivity site (<a href="http://orca.science.oregonstate.edu/npp_products.php">http://orca.science.oregonstate.edu/npp_products.php</a>). The raw MODIS satellite-based euphotic depth estimates are available from the NASA L3 browser (<a href="https://oceancolor.gsfc.nasa.gov/l3/">https://oceancolor.gsfc.nasa.gov/l3/</a>). The original ship-based estimates of net primary productivity are available from the Pangaea (<a href="https://doi.pangaea.de/10.1594/PANGAEA.932417">https://doi.pangaea.de/10.1594/PANGAEA.932417</a>) and the British Oceanography Data Centre (<a href="https://www.bco-dmo.org/dataset/814803">https://www.bco-dmo.org/dataset/814803</a>).</p> <p><strong>Please cite as: </strong></p> <p>Stoer, A., and Fennel, K. 2022. Processing and Data for Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats. Zenodo. doi: 10.5281/zenodo.6977161.</p> <p><strong>Python/MATLAB Software Description: </strong></p> <p>dielFit_GOPeqCR.m: This code is from Johnson and Bif (2021). We have added outputs for standard errors for linear and PvE models and sunrise/sunset times. To run this code with the associated Python software a MATLAB engine needs to be installed. Please see: <a href="https://www.mathworks.com/help/matlab/matlab-engine-for-python.html">https://www.mathworks.com/help/matlab/matlab-engine-for-python.html</a></p> <p>argo_so_processing_20220815.py: This code is the first of two pieces of software for estimating net primary productivity from floats in the Southern Ocean. The program below obtains the data from the BGC Argo database (Argo, 2021) and processes it. Simple data quality control, interpolation, biogeochemical calculations, and data binning occur. The processed float data is located in the folder 'Processed Argo Transects'.</p> <p>argo_daily_npp_20220815.py: This code using processed Argo float data that contains oxygen and particle backscatter measurements to infer net primary production. The code combines the float that meet the criteria of sampling at all local hours of the day throughout its lifetime. Then, it constructs diel cycles from this data by finding the median value of each hour and uses the code from Johnson and Bif (2021), which is a modified version from Barone et al. (2019). The algorithm used to convert particle backscatter to particulate organic carbon is from Graff et al. (2015). We assume that dissolved primary productivity accounts for 30% of total primary productivity (Moran et al., 2022).</p> <p>argo_daily_npp_bootstrap_20220815.py: This code using processed Argo float data that contains co-located oxygen and particle backscatter measurements to infer net primary production. This code is very similar to argo_daily_npp_20220815.py but randomly samples a subset of the co-located profiles at different sample sizes before calculating net primary productivity. Productivity is calculated at each sample size 1000 times. The results of this analysis is located in the folder 'Bootstrapped Results'. </p> <p>More details can be found in the code itself. </p> <p><strong>Data Descriptions: </strong></p> Data from 'Processed Argo Transects' Folder | Description for each variable <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>depth</td> <td>Average depth of depth bin</td> <td>m</td> </tr> <tr> <td>mid_depth</td> <td>Center of depth bin</td> <td>m</td> </tr> <tr> <td>pressure</td> <td>Average pressure in depth bin</td> <td>dbar</td> </tr> <tr> <td>profile_index</td> <td>Profile number or index</td> <td> </td> </tr> <tr> <td>profile_longitude</td> <td>Average longitude of profile</td> <td>degE</td> </tr> <tr> <td>profile_latitude</td> <td>Average latitude of profile</td> <td>degN</td> </tr> <tr> <td>profile_time</td> <td>Average UTC time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_time</td> <td>Average local time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_hour</td> <td>The hour of the local timestamp</td> <td> </td> </tr> <tr> <td>salinity</td> <td>Seawater salinity</td> <td>PSU</td> </tr> <tr> <td>temperature </td> <td>Seawater temperature</td> <td>degC</td> </tr> <tr> <td>oxygen</td> <td>Dissolved oxygen concentration</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_saturation</td> <td>Saturated dissolved oxygen concentration calculated from the Garcia and Gordon (1992) equation.</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_anom</td> <td>The difference between observed dissolved oxygen concentration and saturated oxygen </td> <td>umol kg-1</td> </tr> <tr> <td>bbp470</td> <td>Optical backscatter coefficient at 470 nm. Particulate organic carbon is calculated in argo_daily_npp_20220815.py</td> <td>m-1</td> </tr> </tbody> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>wmo</td> <td>WMO number of float</td> <td> </td> </tr> <tr> <td>profile_index</td> <td>Profile index or profile number taken by float</td> <td> </td> </tr> <tr> <td>profile_latitude</td> <td>Average profile latitude</td> <td>degN</td> </tr> <tr> <td>profile_longitude</td> <td>Average profile longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>fod</td> <td>Fraction of day</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Sinusoidal curve fit to oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc</td> <td>Sinusoidal curve fit to particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>oxy_med</td> <td>Hourly median oxygen</td> <td>mol m-3</td> </tr> <tr> <td>oxy_sem</td> <td>Hourly standard error of oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc_med</td> <td>Hourly median particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>poc_sem</td> <td>Hourly standard error of particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N, co-located)</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N) </td> <td> </td> </tr> <tr> <td>depth</td> <td>Depth of profile</td> <td>m</td> </tr> <tr> <td>zeu</td> <td>1% euphotic depth from Lee et al. (2013) algorithm from NASA (2022) L3 satellite products. </td> <td>m</td> </tr> <tr> <td>n_profiles_bpp</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>n_profiles_oxy</td> <td>Number of oxygen profiles</td> <td> </td> </tr> <tr> <td>n_floats_bbp</td> <td>Number of floats with backscatter measurements</td> <td> </td> </tr> <tr> <td>n_floats_oxy</td> <td>Number of floats with oxygen measurements</td> <td> </td> </tr> <tr> <td>gop_do</td> <td>Gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_serr</td> <td>Standard error of gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly oxygen data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly oxygen data</td> <td> </td> </tr> <tr> <td>oxy_sr</td> <td>The calculated sunrise time as a fraction of the day</td> <td> </td> </tr> <tr> <td>oxy_ss</td> <td>The calculated sunset time as a fraction of the day</td> <td> </td> </tr> <tr> <td>gpp_bbp</td> <td>Gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gpp_bbp_serr</td> <td>Standard error of gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_bbp</td> <td>Gross oxygen productivity calculated from gross carbon productivity (gpp_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_bbp_serr</td> <td>Standard error of gross oxygen productivity calculated from gross carbon productivity (gpp_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp</td> <td>Net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp_serr</td> <td>Standard error of net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do</td> <td>Net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do_serr</td> <td>Standard error of net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do_serr)</td> <td>mol m-3 yr-1</td> </tr> </tbody> </table> <table> </table> Data for Fig. S1 | Description for number_of_bbp_profiles_in_each_year.csv and number_of_oxy_profiles_in_each_year.csv <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>year</td> <td>Year</td> <td> </td> </tr> <tr> <td>bbp470</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>oxygen_anom</td> <td>Number of oxygen profiles</td> <td> </td> </tr> </tbody> </table> <table> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>mid_depth</td> <td>Depth of NPP profile</td> <td>m</td> </tr> <tr> <td>mean</td> <td>Mean volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>median</td> <td>Median volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>min</td> <td>Minimum volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>maximum</td> <td>Maximum volumetric 14C-NPP</td> <td>mmol m-3 yr-1</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th><strong>Variable</strong></th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>subset</td> <td>Number of profiles randomly sampled from the co-located dataset</td> <td> </td> </tr> <tr> <td>int_npp_do</td> <td>Euphotic-depth-integrated net primary productivity calculated from oxygen-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>int_npp_bbp</td> <td>Euphotic-depth-integrated net primary productivity calculated from backscatter-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>gop_do_r2</td> <td>R-squared of the sinusoidal curve to the diel cycle of oxygen anomaly</td> <td> </td> </tr> <tr> <td>gpp_bbp_r2</td> <td>R-squared of sinusoidal curve to the diel cycle of particulate organic carbon</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>ROSE</td> <td>Topographic (negative values are below sea level)</td> <td>m</td> </tr> <tr> <td>ETOPO05_Y</td> <td>Latitude</td> <td>degN</td> </tr> <tr> <td>ETOPO05_X</td> <td>Longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>database</td> <td>Database the data was extracted from</td> <td> </td> </tr> <tr> <td>Month</td> <td>Month of NPP measurement</td> <td>month of year</td> </tr> <tr> <td>npp_14c</td> <td>Net primary productivity estimated from the radiocarbon method</td> <td>mmol m-3 y-1</td> </tr> <tr> <td>depth</td> <td>depth of 14C-NPP measurement</td> <td>m</td> </tr> </tbody> </table> <table> </table>
Exploring the Relationship Between Upper Ocean States and the Falling Ice Radiative Effects using ECCO Product and Global Climate Models
<p><strong><span>Sensitivity test using CESM1-CAM5 following CMIP5 protocool from 1980-2005</span></strong></p> <p><strong><span>NOS: no falling ice radiative effects (FIREs), four data sets</span></strong></p> <p><strong><span>SON: with FIREs, for data sets</span></strong></p> <p><strong><span> Xsize = 362 Ysize = 182 Zsize = 18</span></strong></p> <p><strong><span>Format: netcdf</span></strong></p> <p><strong><span>Upper 200 meter ocean variables</span></strong></p> <p><strong><span>Annual mean (ANN)</span></strong></p> <p><strong><span>CESM2-var-NOS (or SON)-ANN.nc, var = (UO, VO, WO, TO) = (zonal velocity, meridional velocity, ascending velocity, potential temperature) : (cm/s, cm/s, cm/s, K)</span></strong></p>
University of Tromso Arctic Ocean freeboard and snow depth product from CryoSat-2, AltiKa and ICESat-2
<p>Dual-frequency snow depth estimates for the Arctic Ocean in Oct-Apr 2018-2023 derived from gridded 25-km resolution CryoSat-2 and SARAL AltiKa radar freeboards and ICESat-2 laser freeboards. Waveform modelling approach applied to radar altimeters, ICESat-2 laser altimetry freeboards from ATL20 r004. See acompanying publication in The Cryosphere for further details.</p>
Intermediate data products for: Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar (Zippel et al. 2021, Journal of Atmospheric and Oceanic Technology)
<p>This repository contains some of the intermediate data products needed to reproduce the results in the <em>Journal of Atmospheric and Oceanic Technology</em> article "Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar" by S.F. Zippel, J. T. Farrar, C. J. Zappa, U. Miller, L. St. Laurent, T. Ijichi, R. A. Weller, L. McRaven, S. Nylund, and D. Le Bel. Specifically, this material should allow reproduction of Figures 3, 5-7, 12 and 13. Reproduction of Figures 8-11 also requires data from associated glider deployments nearr the SPURS-1 mooring, which may be requested from co-author L. St. Laurent.</p> <p>Code to do the analysis and make the plots is here: https://github.com/zippelsf/MooredTurbulenceMeasurements</p> <p>Matlab data files:</p> <p>(1) 677404_burst1865.mat</p> <p>Single-burst data used for the example spectral fit in Figure 7. The burst was collected during the SPURS-1 project at 21.5m depth. The data collection and processing methods are described in detail in Section 2. </p> <p>(2) 811604_burst0510.mat (Single-burst data used in the unwrapping example, Figure 5)</p> <p>(3) 8116_dissipation_timeseries.mat (Used for associated ancillary data in Figure 6)</p> <p>(4) 913411_burst2879.mat (Single-burst data, used for ancillary data to make Figure 3).</p> <p>(5) BuoyancyFlux_b.mat</p> <p>Ocean buoyancy flux estimates for SPURS-2 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(6) BuoyancyFlux_c.mat</p> <p>Ocean buoyancy flux estimates for SPURS-1 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(7) SPURS1_dissipation_grid_v1d.mat</p> <p>Gridded TKE dissipation rates for SPURS-1 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 8-13. Dissipation rates also available on NASA's PODAAC.</p> <p>(8) spurs1_met_1hr.mat (Processed met data from SPURS-1 mooring. Also available on WHOI's UOP website.)</p> <p>(9) SPURS2_dissipation_grid_v1c.mat</p> <p>Gridded TKE dissipation rates for SPURS-2 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 12. Dissipation rates also available on NASA's PODAAC.</p>
Data, Sensitivity of 21st-century projected ocean new production changes to idealized biogeochemical model structure
<p>Data for reproducing figures in journal article submitted to Biogeosciences in December 2020.</p> <p>Data generated from global 1-degree simulations of the CESM in an ocean-ice configuration.</p> <p>NP model by Brett. See 10.5281/zenodo.4361705 for code for NP model and to use this dataset to recreate paper figures.</p>
Data supporting "A comprehensive analysis of air-sea CO2 flux uncertainties constructed from surface ocean data products"
<p>Changelog</p> <p>v2: Fixes an identified issue in FluxEngine v4.0.7 that affects the calculation of fCO2atm. Fluxes have been recalculated using FluxEngine v4.0.9.1, and the analysis regenerated. The intergrated air-sea CO2 flux (or ocean sink) has reduced by ~0.2-0.3Pg C yr-1 but uncertainties are unchanged. </p> <p>v1: Initial dataset released along with the supporting manuscript</p> <p> </p> <p>Data included in this repository supports the manuscript "A comprehensive analysis of air-sea CO<sub>2</sub> flux uncertainties constructed from surface ocean data products".</p> <p>Two files are present:</p> <ol> <li>A Python config file used to run the software developed for the analysis (Ford et al., 2024)</li> <li>A ZIP file containing the input, neural network, and output files for the analysis.</li> </ol> <p>Within the ZIP file, multiple folders are present:</p> <ol> <li>Decorrelation contains .csv files that contain the annual estimates of the decorrelation lengths for the parameters requiring these (SST, sea ice, wind, fCO<sub>2</sub> and fCO<sub>2</sub> network).</li> <li>Flux contains the individual FluxEngine output files that provide all the flux calculations, and auxillary data to the flux calculations.</li> <li>Fluxengine_input contains the input files to FluxEngine, which specifies the fCO<sub>2 (sw), </sub>xCO<sub>2 (atm)</sub> and the temperature, salinities for the skin and subskin layers.</li> <li>Inputs contains all the monthly 1 degree input data used. Many of the data used are not native monthly 1 deg, and so these are generated from the higher resolution data. These are all combined into the neural_network_input.nc file, so a single file can be distributed with all the inputs used.</li> <li>Networks contains the TensorFlow neural network (FNN) files, where each province has 10 folders (one for each ensemble).</li> <li>Plots contains output plots for debugging and final plots of uncertainties</li> <li>Scalars contains the scalars used to normalise the data before input into the neural network. These are saved as Python pickle files, as they are needed if the neural network is used on other data.</li> <li>Unc_lut contains the look up tables to generate the parameter uncertainty as described in the manuscript. These are Python pickle files.</li> <li>Validation contains a csv file with the independent test RMSD, along with Python Pickle files of the validation data.</li> </ol> <p>In the main folder, three files are present:</p> <ol> <li>Annual_flux.csv contains the annual air-sea CO<sub>2</sub> flux (or ocean sink estimate) estimated from the fCO<sub>2 (sw)</sub> fields. This also contains the annual integrated uncertainties for each component in the uncertainty flow chart in the manuscript.</li> <li>Output.nc contrains the gridded global fields of the fCO<sub>2 (sw)</sub>, the air-sea CO<sub>2</sub> flux, and the uncertainties for all the individual components. Metadata within the file should provide all the information required.</li> <li>Training.tsv contains the training/validation data alongside the input parameters for neural network training</li> </ol> <p> </p> <p>Please contact Daniel J. Ford (<a href="mailto:d.ford@exeter.ac.uk">d.ford@exeter.ac.uk</a>) if you have any questions.</p> <p><strong>Acknowledgements</strong></p> <p>This work was funded by the Convex Seascape Survey (https://convexseascapesurvey.com/) and the European Union under grant agreement no. 101083922 (OceanICU; https://ocean-icu.eu/) and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 10063673, 10064020, 10059241, 10079684, 10059012, 10048179]. The views, opinions and practices used to produce this dataset/software are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p> </p> <p><strong>References</strong></p> <p>Ford, D. J., Blannin, J., Watts, J., Watson, A. J., Landschutzer, P., Jersild, A., & Shutler, J. D. (2024, June 30). OceanICU Neural Network Framework with per pixel uncertainty propagation (v1.1) (Version v1.1). Zenodo. https://doi.org/10.5281/ZENODO.12597803</p>
Particle trajectories - Freilich et al. "Diversity of growth rates maximizes phytoplankton productivity in an eddying ocean"
<p>The files provided here are the offline particle trajectories analyzed in Freilich, Flierl, and Mahadevan “Diversity of growth rates maximizes phytoplankton productivity in an eddying ocean”</p> <p>Both files are sqlite databases containing information about the same particle trajectories which are identified by the variable “iD”</p> <p> </p> <p>ini_day135_z115_forward_biology.db contains nutrient concentration on particle trajectories. A different biological rate lambda is used for each variable denoted NX where X is 0-13. The rates are: 0.015,0.075,0.15,0.3,0.75,1.5,3,10,15,20,50,75,100,120</p> <p>The variable DOY is the model day. </p> <p> </p> <p>ini_day135_z115_physical_forward.db contains the physical variables on particle trajectories. The variables are:</p> <p>x - east-west position</p> <p>y - north-south position</p> <p>z - vertical position</p> <p>u - east-west velocity</p> <p>v - north-south velocity</p> <p>w - vertical velocity</p> <p>vorticity - vertical component of relative vorticity</p>
The importance of turbulent ocean-sea ice nutrient exchanges for simulation of ice algal biomass and production with CICE6.1 and Icepack 1.2 - paper figures
<p>This file contains the figures to the paper "The importance of turbulent ocean-sea ice nutrient exchanges for simulation of ice algal biomass and production with CICE6.1 and Icepack 1.2 - model simulations", including the supplementary ones, in jpg format at 300 dpi.</p>
Model output for "Impact of intensifying nitrogen limitation of ocean net primary production is fingerprinted by nitrogen isotopes"
<p><strong>Description.</strong></p> <p>The data included in this repository is output of simulations performed with the NEMO-PISCESv2 global ocean-biogeochemical model. Simulations involved forcing the NEMO-PISCESv2 with global warming associated with historical and future emissions, as well as the historical and future trends in atmospheric nitrogen deposition. Future climate change was according to the Representative Concentration Pathway 8.5 scenario (Dufresne et al., 2013; Riahi et al., 2011), which sees rapid warming during the 21<sup>st</sup> century. Historical and future atmospheric nitrogen deposition fields were created via linear interpolation of fields produced by Hauglustaine et al. (2014) at years 1850, 2000, 2030, 2050 and 2100. To represent the amplification of deposition since 1950 (Galloway 2014), 60 % of the increase between 1850 and 2000 occurred from 1950 onwards.</p> <p>In this study, we quantified the effect anthropogenic climate change and anthropogenic increases in atmospheric nitrogen deposition on the marine nitrogen cycle. The response of the marine nitrogen cycle to these combined stressors is highly uncertain, and we therefore employed this complex model with a strong representation of nitrogen cycling in an attempt to constrain the global behaviour of this important cycle. In addition, through the addition of nitrogen isotopes to the ocean-biogeochemical model, we also explored and described how the isotopes responded to these anthropogenic forcings, and if the isotopes uniquely fingerprinted the response for potential monitoring/detection purposes.</p> <p>Our abstract reads:</p> <p>“The open ocean nitrogen cycle is being altered by increases in anthropogenic atmospheric nitrogen deposition and climate change. How the nitrogen cycle responds will determine long-term trends in net primary production (NPP) in the nitrogen-limited low latitude ocean, but is poorly constrained by uncertainty in how the source-sink balance will evolve. Here we show that intensifying nitrogen limitation of phytoplankton, associated with near-term reductions in NPP, causes detectable declines in nitrogen isotopes (δ<sup>15</sup>N) and constitutes the primary perturbation of the 21<sup>st</sup> century nitrogen cycle. Model experiments show that ~75% of the low latitude twilight zone develops anomalously low δ<sup>15</sup>N by 2060, predominantly due to the effects of climate change that alter ocean circulation, with implications for the nitrogen sources-sink balance. Our results highlight that δ<sup>15</sup>N changes in the low latitude twilight zone may provide a useful constraint on emerging changes to nitrogen limitation and NPP over the 21<sup>st</sup> century.”</p> <p> </p> <p><strong>Coordinates</strong></p> <p>Spatial resolution is global (90°S-90°N, 180°W-180°E, surface ocean to 5000 metres depth) and temporal resolution runs from years 1801 to 2100.</p> <p> </p> <p><strong>Citation.</strong></p> <p>Buchanan PJ, Aumont O, Bopp L, Mahaffey C, and Tagliabue A (2021): An isotopic fingerprint of increasingly nitrogen-limited phytoplankton in a changing oceanic nitrogen cycle. Nature Communications.</p> <p> </p> <p><strong>Files provided.</strong></p> <p>The data files provided are those that are required to create the figures for this study and/or perform key analyses (i.e. the time of emergence calculations). In the following, each figure or analysis has an associated python script and we list the data files needed to run that script.</p> <p>Python scripts can be found the lead authors GitHub at <a href="https://github.com/pearseb/PISCESiso_Ncycle_analysis">https://github.com/pearseb/PISCESiso_Ncycle_analysis</a>. </p> <p> </p> <p>Put δ<sup>15</sup>N<sub>NO3</sub> observations on model grid (<em>process-d15Nno3_observations_on_model_grid.py</em>):</p> <ul> <li>“RafterTuerena_watercolumn_d15N_no3.txt”</li> </ul> <p>Model assessment (<em>process-model_assessment.py</em>):</p> <ul> <li>“ETOPO_spinup_d15Nno3.nc”</li> <li>“ETOPO_ORCA2.0_Basins_float.nc”</li> <li>“ETOPO_ORCA2.0.full_grid.nc”</li> <li>“RafterTuerena_watercolumn_d15N_no3_gridded.npz”</li> </ul> <p>Time of emergence calculations (<em>process-compute_toe.py</em>):</p> <ul> <li>“ETOPO_picontrol_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_npp.nc”</li> <li>“ETOPO_picontrol_ndep_1y_npp.nc”</li> <li>“ETOPO_future_1y_npp.nc”</li> <li>“ETOPO_future_ndep_1y_npp.nc”</li> <li>“ETOPO_picontrol_1y_nfix.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nfix.nc”</li> <li>“ETOPO_future_1y_nfix.nc”</li> <li>“ETOPO_future_ndep_1y_nfix.nc”</li> </ul> <p>Figure 1 (<em>fig-main1.py</em>):</p> <ul> <li>“ncycle_changes.nc”</li> <li>“sources_and_sinks.nc”</li> </ul> <p>Figure 2 (<em>fig-main2.py</em>):</p> <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> <p>Figure 3 (<em>fig-main3.py</em>):</p> <ul> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_fluxanalysis_results.nc”</li> <li>“figure2D_cc_din_e15n.nc”</li> </ul> <p>Figure 4 (<em>fig-main4.py</em>):</p> <ul> <li>“ETOPO_direct_indirect_effects.nc”</li> </ul> <p>Supp Figure 1 (<em>fig-supp1.py</em>):</p> <ul> <li>“figure_d15Nmaps.nc”</li> </ul> <p>Supp Figure 2 (<em>process-model_assessment.py</em>):</p> <ul> <li>Produced by <em>process-model_assessment.py </em>(see data above)</li> </ul> <p>Supp Figure 3 (<em>fig-supp3.py</em>):</p> <ul> <li>“d15nstats.txt”</li> </ul> <p>Supp Figure 4 (<em>fig-supp4.py</em>):</p> <ul> <li>“ndep_Tg_yr.nc”</li> </ul> <p>Supp Figure 5 (<em>fig-supp5.py</em>):y</p> <ul> <li>“ncycle_changes_climatechangeonly.nc”</li> </ul> <p>Supp Figure 6 (<em>fig-supp6.py</em>):</p> <ul> <li>“ncycle_changes_ndeponly.nc”</li> </ul> <p>Supp Figure 7 (<em>fig-supp7.py</em>):</p> <ul> <li>“figure_depthzones.nc”</li> </ul> <p>Supp Figure 8 (<em>fig-supp8.py</em>):</p> <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> <p>Supp Figure 9 (<em>fig-supp9.py</em>):</p> <ul> <li>“figure2D_ndep_no3_utz.nc”</li> </ul> <p>Supp Figures 10 and 11 (<em>process-0D_model_phyto_frac.py</em>):</p> <ul> <li>Produced by <em>process-0D_model_phyto_frac.py</em> and no data required.</li> </ul> <p>Supp Figure 12 (<em>process-compute_toe.py</em>):</p> <ul> <li>Produced by <em>process-compute_toe.py </em>(see data above)</li> </ul> <p> </p> <p><strong>References.</strong></p> <p>Dufresne, J. L., Foujols, M. A., Denvil, S., Caubel, A., Marti, O., Aumont, O., et al. (2013). <em>Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5</em>. <em>Climate Dynamics</em> (Vol. 40). https://doi.org/10.1007/s00382-012-1636-1</p> <p>Galloway, J. N. (2014). The Global Nitrogen Cycle. In <em>Treatise on Geochemistry</em> (2nd ed., Vol. 10, pp. 475–498). Elsevier. https://doi.org/10.1016/B978-0-08-095975-7.00812-3</p> <p>Hauglustaine, D. A., Balkanski, Y., & Schulz, M. (2014). A global model simulation of present and future nitrate aerosols and their direct radiative forcing of climate. <em>Atmospheric Chemistry and Physics</em>, <em>14</em>(20), 11031–11063. https://doi.org/10.5194/acp-14-11031-2014</p> <p>Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., et al. (2011). RCP 8.5—A scenario of comparatively high greenhouse gas emissions. <em>Climatic Change</em>, <em>109</em>(1–2), 33–57. https://doi.org/10.1007/s10584-011-0149-y</p>
Manganese Limitation of Phytoplankton Physiology and Productivity in the Southern Ocean
<p>Output to accompany:</p> <p>Manganese Limitation of Phytoplankton Physiology and Productivity in the Southern Ocean</p> <p><a href="https://agupubs.onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Hawco%2C+Nicholas+J">Nicholas J. Hawco*</a>, <a href="https://agupubs.onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Tagliabue%2C+Alessandro">Alessandro Tagliabue*</a>, <a href="https://agupubs.onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Twining%2C+Benjamin+S">Benjamin S. Twining</a></p> <p>* joint first authors</p> <p>First published: 25 October 2022</p> <p><a href="https://doi.org/10.1029/2022GB007382">https://doi.org/10.1029/2022GB007382</a></p> <p>Netcdf file details:<br> <br> BYONIC8R1 = standard run<br> !<br> BYONIC6R1 = as BYONIC8R1 but no zn-mn interaction<br> BYONIC3R1 = as BYONIC8R1 but no Mn limitation<br> BYONIC7R1 = as BYONIC8R1 but lower mnchl<br> BYONIC2R1 = as BYONIC8R1 but higher mnchl<br> BYONIC5R1 = as BYONIC8R1 but w/ QZn feedback<br> BYONIC12R1 = BYONIC8R1 LGM<br> BYONIC10R1 = no Mn lim LGM</p> <p>All on native ORCA2 NEMO grid</p>
Model output for "Enrichment of ammonium in the future ocean threatens diatom productivity"
<p>Each netcdf file (.nc) contains model output from simulations performed with the<br> NEMO-PISCES global ocean-biogeochemistry model. These simulations were<br> forced by physical output from the IPSL-CM5A Earth System Model, which <br> performed both the natural (no anthropogenic activities) and RCP8.5 scenarios.</p> <p>Variables in spin-up "ptrc" files are:</p> <p> name title I J K L<br> PHY (Nano)Phytoplankton Concentrati 1:360 1:180 1:31 1:12<br> PHY2 Diatoms Concentration 1:360 1:180 1:31 1:12<br> O2 Oxygen Concentration 1:360 1:180 1:31 1:12<br> PREO2 Abiotic Oxygen Concentration 1:360 1:180 1:31 1:12<br> FER Dissolved Iron Concentration 1:360 1:180 1:31 1:12<br> NO3 Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2 Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4 Ammonium Concentration 1:360 1:180 1:31 1:12<br> NO3_15 15N Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_15 15N Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4_15 15N Ammonium Concentration 1:360 1:180 1:31 1:12<br> O2_18 18O Dissolved Oxygen Concentrat 1:360 1:180 1:31 1:12<br> NO3_18 18O Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_18 18O Nitrite Concentration 1:360 1:180 1:31 1:12</p> <p> </p> <p>Variables in scenario "ptrc" files are:</p> <p> name title I J K L<br> PHY (Nano)Phytoplankton Concentrati 1:360 1:180 1:31 1:12<br> PHY2 Diatoms Concentration 1:360 1:180 1:31 1:12<br> ZOO (Micro)Zooplankton Concentratio 1:360 1:180 1:31 1:12<br> ZOO2 Mesozooplankton Concentration 1:360 1:180 1:31 1:12<br> O2 Oxygen Concentration 1:360 1:180 1:31 1:12<br> PREO2 Abiotic Oxygen Concentration 1:360 1:180 1:31 1:12<br> FER Dissolved Iron Concentration 1:360 1:180 1:31 1:12<br> NO3 Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2 Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4 Ammonium Concentration 1:360 1:180 1:31 1:12<br> DOC Dissolved organic Concentration 1:360 1:180 1:31 1:12<br> POC Small organic carbon Concentrat 1:360 1:180 1:31 1:12<br> GOC Big organic carbon Concentratio 1:360 1:180 1:31 1:12<br> NO3_15 15N Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_15 15N Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4_15 15N Ammonium Concentration 1:360 1:180 1:31 1:12<br> PHY_15 15N Nanophytoplankton Concentra 1:360 1:180 1:31 1:12<br> PHY2_15 15N Diatoms Concentration 1:360 1:180 1:31 1:12<br> DOC_15 15N Dissolved organic Concentra 1:360 1:180 1:31 1:12<br> POC_15 15N Small particulate Concentra 1:360 1:180 1:31 1:12<br> GOC_15 15N Large particulate Concentra 1:360 1:180 1:31 1:12<br> ZOO_15 15N Microzooplankton Concentrat 1:360 1:180 1:31 1:12<br> ZOO2_15 15N Mesozooplankton Concentrati 1:360 1:180 1:31 1:12<br> O2_18 18O Dissolved Oxygen Concentrat 1:360 1:180 1:31 1:12<br> NO3_18 18O Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_18 18O Nitrite Concentration 1:360 1:180 1:31 1:12</p> <p>Variables in the scenario "diad" files are:</p> <p> name title I J K L<br> PH PH 1:360 1:180 1:31 1:12<br> HEUP Euphotic layer depth 1:360 1:180 ... 1:12<br> PAR Photosynthetically Available Ra 1:360 1:180 1:31 1:12<br> PARDM Daily mean PAR 1:360 1:180 1:31 1:12<br> PPPHYN Primary production of nanophyto 1:360 1:180 1:31 1:12<br> PPPHYD Primary production of diatoms 1:360 1:180 1:31 1:12<br> PPNEWN New Primary production of nanop 1:360 1:180 1:31 1:12<br> PPNEWD New Primary production of diato 1:360 1:180 1:31 1:12<br> PPNO2N NO2 Primary production of nanop 1:360 1:180 1:31 1:12<br> PPNO2D NO2 Primary production of diato 1:360 1:180 1:31 1:12<br> NITRNH4 Ammonia-oxidation rate (NH4-->N 1:360 1:180 1:31 1:12<br> NITRNO2 Nitrite-oxidation rate (NO2-->N 1:360 1:180 1:31 1:12<br> MUAOA Growth rate of ammonia oxidiser 1:360 1:180 1:31 1:12<br> MUAOAMAX Max potential ammonia oxidation 1:360 1:180 1:31 1:12<br> LAOANH4 Substrate limitation of NH4 oxi 1:360 1:180 1:31 1:12<br> LAOAFER Iron limitation of NH4 oxidatio 1:360 1:180 1:31 1:12<br> LAOAPAR Light limitation of NH4 oxidati 1:360 1:180 1:31 1:12<br> LAOAPH pH limitation of NH4 oxidation 1:360 1:180 1:31 1:12<br> LNOBNO2 Substrate limitation of NO2 oxi 1:360 1:180 1:31 1:12<br> LNOBFER Iron limitation of NO2 oxidatio 1:360 1:180 1:31 1:12<br> LNOBPAR Light limitation of NO2 oxidati 1:360 1:180 1:31 1:12<br> NFIX Nitrogen fixation 1:360 1:180 1:31 1:12<br> RIVER_NO3<br> Nitrate added by rivers 1:360 1:180 ... 1:12<br> NDEP_NO3 Nitrate added by deposition 1:360 1:180 ... 1:12<br> REMIN Oxic remineralization of OM (DO 1:360 1:180 1:31 1:12<br> EXCR1 Excretion by microzooplankton 1:360 1:180 1:31 1:12<br> EXCR2 Excretion by mesozooplankton 1:360 1:180 1:31 1:12<br> DENITNO3 Denitrification rate (NO3-->NO2 1:360 1:180 1:31 1:12<br> DENITNO2 Denitrification rate (NO2-->N2) 1:360 1:180 1:31 1:12<br> ANAMMOX Anaerobic oxidation of NH4 (NH4 1:360 1:180 1:31 1:12<br> ALTREM Alternative anaerobic remin (DO 1:360 1:180 1:31 1:12<br> SDEN3D Sed denitrification of OM (NO3- 1:360 1:180 1:31 1:12<br> SREM3D Sed remineralisation of OM (DOC 1:360 1:180 1:31 1:12<br> <br> Files:</p> <ul> <li> ETOPO_nitr_kaoafer00_1m_ptrc.nc</li> <li> ETOPO_nitr_kaoafer00_1m_diad.nc</li> <li> ETOPO_nitr_kaoafer00_2ndpicontrol_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_2ndpicontrol_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acid_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acid_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_warm_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_warm_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_circ_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_circ_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_full_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_full_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_picontrolalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_picontrolalt_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acidalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acidalt_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_fullalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_fullalt_1m_diad_2081-2100_ave.nc</li> </ul> <p> </p> <p>Naming convention:<br> "ETOPO" - refers to being on a regular 1x1 degree horizontal grid<br> "nitri" - refers to the developed PISCES model with explicit two-step nitrification<br> "kaoafer00" - refers to no iron limitation of AOA <br> "1m" - refers to the timestep resolution, here 1 month. Thus, all data presented here is monthly averaged values.<br> "2ndpicontrol" - refers to preindustrial control run<br> "acid" - refers to the control run + ocean acidification<br> "warm" - refers to the control run + warming<br> "circ" - refers to the control run + circulation change<br> "full" - refers to the control run + ocean acidification + warming + circulation change<br> "picontrolalt" - refers to preindustrial control run (alternative pH parameterisation)<br> "acidalt" - refers to the control run + ocean acidification (alternative pH parameterisation)<br> "fullalt" - refers to the control run + ocean acidification + warming + circulation change (alternative pH parameterisation)<br> <br> Contact: Pearse.Buchanan@liverpool.ac.uk or pbuchanan@carnegiescience.edu</p> <p> </p>
An archive of net community production estimates derived from autonomous profiler observations at Ocean Station Papa
<p>This dataset contains an archive of marine net community production estimates for Ocean Station Papa, derived using autonomous profilers, including BGC-Argo floats and sea gliders. The archive contains data from the following original publications:</p> <ol> <li>Haskell, W. Z., Fassbender, A. J., Long, J. S., and Plant, J. N.: Annual Net Community Production of Particulate and Dissolved Organic Carbon From a Decade of Biogeochemical Profiling Float Observations in the Northeast Pacific, Global Biogeochemical Cycles, 34, 1–22, <a href="https://doi.org/10.1029/2020GB006599">https://doi.org/10.1029/2020GB006599</a>, 2020.</li> <li>Huang, Y., Fassbender, A. J., Long, J. S., Johannessen, S., and Bernardi Bif, M.: Partitioning the Export of Distinct Biogenic Carbon Pools in the Northeast Pacific Ocean Using a Biogeochemical Profiling Float, Global Biogeochemical Cycles, 36, <a href="https://doi.org/10.1029/2021gb007178">https://doi.org/10.1029/2021gb007178</a>, 2022.</li> <li>Pelland, N. A., Eriksen, C. C., Emerson, S. R., and Cronin, M. F.: Seaglider Surveys at Ocean Station Papa: Oxygen Kinematics and Upper-Ocean Metabolism, Journal of Geophysical Research: Oceans, 123, 6408–6427, <a href="https://doi.org/10.1029/2018JC014091">https://doi.org/10.1029/2018JC014091</a>, 2018.</li> <li>Plant, J. N., Johnson, K. S., Sakamoto, C. M., Jannasch, H. W., Coletti, L. J., Riser, S. C., and Swift, D. D.: Net community production at Ocean Station Papa observed with nitrate and oxygen sensors on profiling floats, Global Biogeochemical Cycles, 30, 859–879, <a href="https://doi.org/10.1002/2015GB005349">https://doi.org/10.1002/2015GB005349</a>, 2016.</li> <li>Yang, B., Emerson, S. R., and Bushinsky, S. M.: Annual net community production in the subtropical Pacific Ocean from in-situ oxygen measurements on profiling floats, Global Biogeochemical Cycles, 31, 728–744, <a href="https://doi.org/10.1002/2016GB005545">https://doi.org/10.1002/2016GB005545</a>, 2017.</li> </ol> <p> </p> <p><strong>Dataset description:</strong></p> <p>Separate Matlab (.mat) and NetCDF (.nc) files are provided for each original publication dataset. At minimum, each contains time (recorded as absolute days since 01 Jan 2000, 00:00:00), and time-explicit depth-integrated NCP. Additional fields include depth-resolved NCP, and water column depth (Plant et al., 2016; Pelland et al., 2018).</p> <p>Note that the Haskell et al. (2020) dataset (haskell20.mat, haskell20.nc) contains climatological NCP estimates. Accordingly, the time field represents the Year-day only. </p> <p>All NCP units are recorded in units of mmol O2/m2/d (depth-integrated) or mmol O2/m3/d (depth-resolved).</p> <p>Additional information on each dataset is contained within the data files. Additional processing information is contained in Izett et al. (under review).</p> <p> </p> <p><strong>The data were compiled in: </strong></p> <p>Izett, R. W., Fennel, K., Stoer, A. C., and Nicholson, D. P. Reviews and syntheses: Expanding the global coverage of gross primary production and net community production measurements using BGC-Argo floats, <em>Submitted to Biogeosciences. 2023</em>.</p> <p> </p> <p><strong>Please cite as:</strong></p> <p>Izett, R., Haskell, W., Huang, Y., Pelland, N., Plant, J., and Yang, B. 2023. An archive of net community production estimates derived from autonomous profiler observations at Ocean Station Papa. Database. Zenodo. doi: 10.5281/zenodo.7667521. Accessed on <em>DATE</em>. </p>
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