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6 results for “primary production, measured as carbon”
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>
McMurdo Dry Valleys site, station East Lake Bonney, study of primary production, measured as carbon in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from McMurdo Dry Valleys (MCM) contains primary production, measured as carbon measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
McMurdo Dry Valleys site, station West Lake Bonney, study of primary production, measured as carbon in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from McMurdo Dry Valleys (MCM) contains primary production, measured as carbon measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
Palmer Station site, station Palmer Station, study of primary production, measured as carbon in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Palmer Station (PAL) contains primary production, measured as carbon measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
Arctic LTER site, station Toolik Lake, study of primary production, measured as carbon in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Arctic LTER (ARC) contains primary production, measured as carbon measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
California Current Ecosystem site, station Ohman Region: subset of CalCOFI stations inshore and nearshore in the Southern California Bight region; CalCOFI lines 80-93, stations from shore offshore to station 70, study of primary production, measured as carbon in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from California Current Ecosystem (CCE) contains primary production, measured as carbon measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
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