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Dataset Title:  Carbon export from San Dieguito Lagoon from samples for seawater carbonate
biogeochemistry between April 2014 and January 2015
  RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_765108)
Range: longitude = -117.26687 to -117.11851°E, latitude = 32.968033 to 33.06342°N
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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Things You Can Do With Your Graphs

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 32.968031, 33.063419;
    String axis "Y";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude of survey station";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -117.266867, -117.118508;
    String axis "X";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude of survey station";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  Date {
    String description "Date of the survey in DD/MM/YYYY";
    String ioos_category "Time";
    String long_name "Date";
    String units "unitless";
  }
  time2 {
    String description "Time of survey in local time (GMT -8); 24-hour; format: HH:MM";
    String ioos_category "Time";
    String long_name "Time";
    String units "unitless";
  }
  Temp {
    Float32 _FillValue NaN;
    Float32 actual_range 12.4, 27.6;
    String description "Temperature of survey station";
    String ioos_category "Temperature";
    String long_name "Temperature";
    String units "degrees Celsius";
  }
  Sal {
    Float32 _FillValue NaN;
    Float32 actual_range 0.7, 34.5;
    String description "Salinity of survey station";
    String ioos_category "Unknown";
    String long_name "Sal";
    String units "n/a";
  }
  DO_pcnt {
    Float32 _FillValue NaN;
    Float32 actual_range 9.3, 191.2;
    String description "Dissolved oxygen saturation (%)";
    String ioos_category "Unknown";
    String long_name "DO Pcnt";
    String units "unitless (percentage)";
  }
  DIC {
    Float32 _FillValue NaN;
    Float32 actual_range 1944.67, 6822.31;
    String description "Dissolved inorganic carbon of seawater";
    String ioos_category "Unknown";
    String long_name "DIC";
    String units "micromoles per kilogram (umol/kg)";
  }
  TA {
    Float32 _FillValue NaN;
    Float32 actual_range 2223.82, 6798.34;
    String description "Total alkalinity of seawater";
    String ioos_category "Unknown";
    String long_name "TA";
    String units "micromoles per kilogram (umol/kg)";
  }
  TOC {
    Float32 _FillValue NaN;
    Float32 actual_range 70.6, 410.23;
    String description "Dissolved organic carbon of seawater";
    String ioos_category "Unknown";
    String long_name "TOC";
    String units "micromoles per kilogram (umol/kg)";
  }
  PO4 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.05, 7.3;
    String description "Dissolved phosphate of seawater";
    String ioos_category "Dissolved Nutrients";
    String long_name "Mass Concentration Of Phosphate In Sea Water";
    String units "micromoles per kilogram (umol/kg)";
  }
  Si {
    Float32 _FillValue NaN;
    Float32 actual_range 1.15, 353.44;
    String description "Dissolved silicate of seawater";
    String ioos_category "Dissolved Nutrients";
    String long_name "Mass Concentration Of Silicate In Sea Water";
    String units "micromoles per kilogram (umol/kg)";
  }
  NO2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 2.21;
    Float64 colorBarMaximum 1.0;
    Float64 colorBarMinimum 0.0;
    String description "Dissolved nitrite of seawater";
    String ioos_category "Dissolved Nutrients";
    String long_name "Mole Concentration Of Nitrite In Sea Water";
    String units "micromoles per kilogram (umol/kg)";
  }
  NO3 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 5.13;
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Dissolved nitrate of seawater";
    String ioos_category "Dissolved Nutrients";
    String long_name "Mole Concentration Of Nitrate In Sea Water";
    String units "micromoles per kilogram (umol/kg)";
  }
  NH4 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 7.97;
    Float64 colorBarMaximum 5.0;
    Float64 colorBarMinimum 0.0;
    String description "Dissolved ammonium of seawater";
    String ioos_category "Dissolved Nutrients";
    String long_name "Mole Concentration Of Ammonium In Sea Water";
    String units "micromoles per kilogram (umol/kg)";
  }
  ISO_DateTime_Local {
    String description "Date and time of survey formatted to ISO8601 standard: yyyy-mm-ddThh:mm-hh:mm";
    String ioos_category "Time";
    String long_name "ISO Date Time Local";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Methods description:  
 Between April 2014 and January 2015, discrete samples for total alkalinity
(TA), dissolved inorganic carbon (DIC), total organic carbon (TOC), and
nutrients (nitrate, nitrite, ammonium, phosphate, and silicate) were collected
hourly from high tide to low tide at the mouth of SDL, both during the day and
during the night. Samples for TA, DIC, and nutrients were also collected at
ten different sites across the SDL during the day at high tide. For all
samples collected, surface temperature, salinity, and dissolved oxygen were
measured with handheld YSI multiprobe while discrete seawater samples were
collected by hand at the surface following standard protocols.
 
Analytical Methods:  
 Surface seawater samples were collected by hand at ~0.25 m depth using 250
ml Pyrex glass bottles and immediately fixed with 100 \\u00b5L HgCl2 as per
standard protocols (Dickson et al. 2007). A handheld YSI multiprobe Pro2030
was calibrated and used to measure temperature, salinity, and dissolved oxygen
saturation at the time of sampling. All seawater samples were transported to
the Scripps Coastal and Open Ocean Biogeochemistry lab and analyzed for TA via
an open-cell potentiometric acid titration system developed at Scripps
Institution of Oceanography (SIO) by A. Dickson (Dickson et al. 2007) and DIC
via an automated infra-red inorganic carbon analyzer (AIRICA, Marianda Inc).
Nutrient samples were filtered through a 0.45 um filter into acid-leached
HDPE-containers, and kept frozen until analysis. They were analyzed at
Oceanographic Data Facility at SIO
[(http://odf.ucsd.edu](\\\\\"http://\\(http://odf.ucsd.edu\\\\\")) using a Seal
Analytical continuous-flow AutoAnalyzer. TOC samples were collected in
combusted glass-vial and acidified with trace-metal grade HCl. They were
analyzed in the Aluwihare lab at SIO according to Pedler et al. (2014) by
combusting an unfiltered sample and measuring CO2 gas concentration using a
non-dispersive infrared detector.
 
Quality Control:  
 Standard protocols were followed for sampling and analysis of seawater all
parameters. The YSI multiprobe was calibrated prior to each sampling with an
accuracy of \\u00b10.2\\u00b0C for temperature, \\u00b10.3 g kg-1 for salinity,
and \\u00b12% for DO. The mean accuracy (TA\\u00b11 \\u00b5mol kg-1, DIC \\u00b11
\\u00b5mol kg-1) and precision (TA\\u00b12 \\u00b5mol kg-1, DIC \\u00b12 \\u00b5mol
kg-1) of TA and DIC measurements were evaluated using certified reference
materials (CRM) provided by the laboratory of A. Dickson at SIO and analyzed
every 5 samples for DIC and ~10 samples for TA. The uncertainty of the
nutrient measurements were reported as nitrate \\u00b10.05 uM, nitrite
\\u00b10.05 uM, ammonium \\u00b10.03 uM, silicate \\u00b12-4 uM, and phosphate
\\u00b10.004 uM by ODF. The TOC method was reported with an uncertainty of
\\u00b15%.";
    String awards_0_award_nid "737877";
    String awards_0_award_number "OCE-1255042";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1255042";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "Dr Henrietta N Edmonds";
    String awards_0_program_manager_nid "51517";
    String cdm_data_type "Other";
    String comment 
"San Dieguito Lagoon Carbon export 
  PI: Andreas Andersson (UCSD) 
  Version date: 17-April-2019";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.2d  13 Jun 2019";
    String date_created "2019-04-17T19:00:17Z";
    String date_modified "2019-05-07T17:17:37Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.765108.1";
    Float64 Easternmost_Easting -117.118508;
    Float64 geospatial_lat_max 33.063419;
    Float64 geospatial_lat_min 32.968031;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -117.118508;
    Float64 geospatial_lon_min -117.266867;
    String geospatial_lon_units "degrees_east";
    String history 
"2019-07-18T21:48:49Z (local files)
2019-07-18T21:48:49Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_765108.das";
    String infoUrl "https://www.bco-dmo.org/dataset/765108";
    String institution "BCO-DMO";
    String instruments_0_acronym "Shimadzu TOC-V";
    String instruments_0_dataset_instrument_description "TOC samples were collected in 40 mL precombusted borosilicate vials after these were prerinsed with the sample, followed by acidification to ~pH 2 using 12M trace metal-grade HCl, and capped with acid-washed teflon-lined septa. TOC concentrations were quantified by combusting the unfiltered samples and measuring the CO2 released using a non-dispersive infrared detector. The instrument precision was checked using low carbon deep seawater TOC samples.�";
    String instruments_0_dataset_instrument_nid "765120";
    String instruments_0_description "A Shimadzu TOC-V Analyzer measures DOC by high temperature combustion method.";
    String instruments_0_instrument_external_identifier "http://onto.nerc.ac.uk/CAST/124";
    String instruments_0_instrument_name "Shimadzu TOC-V Analyzer";
    String instruments_0_instrument_nid "603";
    String instruments_0_supplied_name "Shimadzu 500 V-CSN/TNM-1 TOC analysis system";
    String instruments_1_acronym "Water Quality Multiprobe";
    String instruments_1_dataset_instrument_description "YSI Handheld Multiparameter Instrument was used to measure in situ temperature (accuracy � 0.15�C), salinity (accuracy � 1%), and DO% (accuracy � 2%).";
    String instruments_1_dataset_instrument_nid "765114";
    String instruments_1_description "An instrument which measures multiple water quality parameters based on the sensor configuration.";
    String instruments_1_instrument_name "Water Quality Multiprobe";
    String instruments_1_instrument_nid "678";
    String instruments_1_supplied_name "Water Quality Multiprobe";
    String instruments_2_acronym "Automatic titrator";
    String instruments_2_dataset_instrument_description "The open-cell potentiometric acid titration system was developed by the laboratory of A.G. Dickson. Briefly, a known amount of seawater is added to an open cell temperature controlled beaker. Hydrochloric acid is added using a Methrom Dosimat to a pH of 3.5-4.0 and allowed to stabilize to remove CO2 gas formed by the addition of acid. Small aliquots of hydrochloric acid are then added to pH of ~ 3.0. The titration is monitored by a glass electrode and the total alkalinity of the sample is calculated using a non-linear least-squares method following Dickson et al. (2007).";
    String instruments_2_dataset_instrument_nid "765116";
    String instruments_2_description "Instruments that incrementally add quantified aliquots of a reagent to a sample until the end-point of a chemical reaction is reached.";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB12/";
    String instruments_2_instrument_name "Automatic titrator";
    String instruments_2_instrument_nid "682";
    String instruments_2_supplied_name "Open-cell potentiometric acid titration system";
    String instruments_3_dataset_instrument_description "The Automated Infra Red Inorganic Carbon Analyzer (AIRICA) utilizes infrared detection of CO2 gas purged from an acidified seawater sample. A high-precision syringe pump extracts the seawater sample, acidifies the sample with phosphoric acid, and analyzes the gas released with an infrared light analyzer (LICOR). The CO2 signal is integrated for each sample to quantify the total inorganic carbon for a given aliquot of seawater analyzed. Three aliquots and peak integrations are performed for each seawater sample and averaged to determine the dissolved inorganic carbon for each sample. Precision was typically �1–2 �mol/kg for TA. Please see http://marianda.com/index.php?site=products&subsite=airica�for a complete instrument description.";
    String instruments_3_dataset_instrument_nid "765115";
    String instruments_3_description "Instruments measuring carbonate in sediments and inorganic carbon in the water column.";
    String instruments_3_instrument_name "Inorganic Carbon Analyzers";
    String instruments_3_instrument_nid "743387";
    String instruments_3_supplied_name "Automated Infra Red Inorganic Carbon Analyzer (AIRICA)";
    String instruments_4_acronym "Seal Analytical AutoAnalyser 3HR";
    String instruments_4_dataset_instrument_description "Nutrient samples were taken according to best practices and were filtered using a 0.4 �m filter and immediately frozen until analysis. Samples were analyzed using US Environmental Protection Agency methods for ammonium (method G-171-96, detection limit 0.034 �mol/L), nitrite+nitrate (method G-172-96, detection limit 0.010 �mol/L), silicate (method G-177-96, detection limit 0.016 �mol/L), and phosphate (method G-297-03, detection limit 0.025 �mol/L). Please see http://seal-analytical.com/Products/AA3SFAAnalyzer/tabid/59/Default.aspx�for a complete instrument description.";
    String instruments_4_dataset_instrument_nid "765119";
    String instruments_4_description "A fully automated Segmented Flow Analysis (SFA) system, ideal for water and seawater analysis. It comprises a modular system which integrates an autosampler, peristaltic pump, chemistry manifold and detector. The sample and reagents are pumped continuously through the chemistry manifold, and air bubbles are introduced at regular intervals forming reaction segments which are mixed using glass coils. The AA3 uses segmented flow analysis principles to reduce inter-sample dispersion, and can analyse up to 100 samples per hour using stable LED light sources.";
    String instruments_4_instrument_name "Seal Analytical AutoAnalyser 3HR";
    String instruments_4_instrument_nid "765117";
    String instruments_4_supplied_name "SEAL AutoAnalyzer 3 four-channel segmented flow analyzer";
    String keywords "altimetry, ammonia, ammonium, bco, bco-dmo, biological, chemical, chemistry, concentration, data, dataset, date, dic, dissolved, dissolved nutrients, dmo, DO_pcnt, earth, Earth Science > Oceans > Ocean Chemistry > Ammonia, Earth Science > Oceans > Ocean Chemistry > Nitrate, Earth Science > Oceans > Ocean Chemistry > Phosphate, Earth Science > Oceans > Ocean Chemistry > Silicate, erddap, iso, ISO_DateTime_Local, laboratory, latitude, local, longitude, management, mass, mass_concentration_of_phosphate_in_sea_water, mass_concentration_of_silicate_in_sea_water, mole, mole_concentration_of_ammonium_in_sea_water, mole_concentration_of_nitrate_in_sea_water, mole_concentration_of_nitrite_in_sea_water, n02, nh4, nitrate, nitrite, NO2, no3, nutrients, ocean, oceanography, oceans, office, pcnt, phosphate, po4, preliminary, sal, satellite, science, sea, seawater, silicate, Temp, temperature, time, time2, toc, water";
    String keywords_vocabulary "GCMD Science Keywords";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    String metadata_source "https://www.bco-dmo.org/api/dataset/765108";
    Float64 Northernmost_Northing 33.063419;
    String param_mapping "{'765108': {'Lat': 'flag - latitude', 'Lon': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/765108/parameters";
    String people_0_affiliation "University of California-San Diego";
    String people_0_affiliation_acronym "UCSD-SIO";
    String people_0_person_name "Andreas Andersson";
    String people_0_person_nid "51444";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI BCO-DMO";
    String people_1_person_name "Shannon Rauch";
    String people_1_person_nid "51498";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "CAREER:   Biogeochemical Modification of Seawater CO2 Chemistry in Near-Shore Environments:   Effect of Ocean Acidification";
    String projects_0_acronym "Nearshore CO2";
    String projects_0_description 
"NSF abstract:
Because of well-known chemical principles, changes in the CO2 chemistry of seawater in the open ocean as a result of rising atmospheric CO2 can be predicted very accurately. On the other hand, in near-shore environments, these projections are much more difficult because the CO2 chemistry is largely modified by biogeochemical processes operating on timescales of hours to months. To make predictions on how near-shore seawater CO2 chemistry will change in response to ocean acidification (OA), it is critical to consider the relative influence of net ecosystem production (NEP) and net ecosystem calcification (NEC), and how these processes might change in response to this major perturbation. Understanding how future OA will alter near-shore seawater CO2 chemistry and variability was identified as a major critical knowledge gap at the recent IPCC WG II/WG I workshop on impacts of ocean acidification on marine biology and ecosystems in January of 2011, and also at the International Ocean Acidification Network workshop in Seattle in June of 2012.
With funding from this CAREER award, a researcher at the Scripps Institute of Oceanography and his students will study how biogeochemical processes and the relative contributions from NEP and NEC modify seawater CO2 chemistry in near-shore environments influenced by different benthic communities under well characterized environmental and physical conditions, and how these processes might change in response to OA. The team will investigate a limited number of contrasting habitats in subtropical (reef crest, back/patch reef, lagoon, seagrass bed, algal mat) and temperate (kelp bed, inter- and sub-tidal, marsh) environments during summer and winter, employing a method that evaluates the function and performance of the carbon cycle of a system using a stoichiometric vector approach based on changes in total dissolved inorganic carbon (DIC) and total alkalinity (TA). These field studies will be complemented by controlled mesocosm experiments with contrasting and mixed benthic communities under different OA scenarios.
The project has two educational components: (1) developing a research-driven OA and biogeochemistry course based on inquiry-, experience-, and collaborative-based learning; and (2) working with the Ocean Discovery Institute (ODI) to engage individuals from a local underrepresented minority community in science through educational activities focused on OA, and also providing a moderate number of internships for high school and college students to engage in this research project.
Broader Impacts: This project will directly support one PhD student, one junior research technician, and two high school and college interns from underrepresented minorities (URM) each summer of the project. It will contribute to the education of 80 undergraduate and graduate students participating in the research based ocean acidification/biogeochemistry course offered four times throughout the duration of the project at SIO/UCSD. Education and curricular material on the topics of OA, including hands-on laboratories, classroom and field-based activities will be developed through the collaboration with the ODI and brought to hundreds of URM students and their teachers in the City Heights area, a community with the highest poverty and ethnic diversity in the San Diego region. This collaboration will enable URM students to directly engage in a rapidly evolving field of research that has high relevance at both the local and global scales. To ensure broad dissemination of this project and the topic of OA, the research team will work with the Google Ocean team to incorporate information and educational material in the Google Ocean Explorer.";
    String projects_0_end_date "2019-05";
    String projects_0_geolocation "San Diego, California; Bermuda; Oahu, Hawaii";
    String projects_0_name "CAREER:   Biogeochemical Modification of Seawater CO2 Chemistry in Near-Shore Environments:   Effect of Ocean Acidification";
    String projects_0_project_nid "737878";
    String projects_0_start_date "2013-06";
    String publisher_name "Shannon Rauch";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 32.968031;
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary "General study design:\\r\\nIn this study, samples for seawater carbonate biogeochemistry were collected in the San Dieguito Lagoon (SDL) over the course of a year, both at the mouth of the lagoon during ebb tide and at stations spatially distributed across the lagoon at high tide. The study was designed to assess the lateral carbon flux from SDL to the coastal ocean, and how this changed under different environmental conditions.";
    String title "Carbon export from San Dieguito Lagoon from samples for seawater carbonate biogeochemistry between April 2014 and January 2015";
    String version "1";
    Float64 Westernmost_Easting -117.266867;
    String xml_source "osprey2erddap.update_xml() v1.5-beta";
  }
}

 

Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its selection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.


 
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