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Dataset Title:  Seawater carbonate chemistry from 13 sites in Palau collected from small boats
in the Palauan archipelago from 2011-2013
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_489014)
Range: longitude = 134.349 to 134.557°E, latitude = 7.158 to 7.577°N
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  site_name {
    String bcodmo_name "Site_Desig";
    String description "Name of the site.";
    String long_name "Site Name";
    String units "dimensionless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 7.158, 7.577;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude of the sampling location.";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range 134.349, 134.557;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude of the sampling location.";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String standard_name "longitude";
    String units "degrees_east";
  }
  date {
    Int32 _FillValue 2147483647;
    Int32 actual_range 20110919, 20131112;
    String bcodmo_name "date_local";
    String description "Date (as year-month-day); in local time zone. in YYYYmmdd format.";
    String long_name "Date";
    String units "unitless";
  }
  time2 {
    String bcodmo_name "time_local";
    String description "Time (hours and minutes); 24-hour clock; local time zone.";
    String long_name "Time";
    String units "HHMM";
  }
  TA {
    Float32 _FillValue NaN;
    Float32 actual_range 1858.702, 2245.173;
    String bcodmo_name "TALK";
    String description "Total alkalinity.";
    String long_name "TA";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/MDMAP014/";
    String units "micromoles per kilogram (umol/kg)";
  }
  DIC {
    Float32 _FillValue NaN;
    Float32 actual_range 1705.471, 1947.879;
    String bcodmo_name "DIC";
    String description "Dissolved inorganic carbon.";
    String long_name "DIC";
    String units "micromoles per kilogram (umol/kg)";
  }
  sal {
    Float32 _FillValue NaN;
    Float32 actual_range 30.022, 34.137;
    String bcodmo_name "sal";
    String description "Salinity.";
    String long_name "Sal";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PSALST01/";
    String units "dimensionless";
  }
  temp {
    Float32 _FillValue NaN;
    Float32 actual_range 26.842, 32.092;
    String bcodmo_name "temperature";
    String description "Water temperature.";
    String long_name "Temperature";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius ( C)";
  }
  pH {
    Float32 _FillValue NaN;
    Float32 actual_range 7.78, 8.164;
    String bcodmo_name "pH";
    Float64 colorBarMaximum 9.0;
    Float64 colorBarMinimum 7.0;
    String description "pH.";
    String long_name "Sea Water Ph Reported On Total Scale";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PHXXZZXX/";
    String units "dimensionless";
  }
  pCO2 {
    Float32 _FillValue NaN;
    Float32 actual_range 264.613, 717.26;
    String bcodmo_name "pCO2";
    String description "Partial pressure of carbon dioxide.";
    String long_name "P CO2";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PCO2C101/";
    String units "micro-atmospheres (uatm)";
  }
  CO2_diss {
    Float32 _FillValue NaN;
    Float32 actual_range 6.842, 18.124;
    String bcodmo_name "unknown";
    String description "Dissolved carbon dioxide (CO2).";
    String long_name "CO2 Diss";
    String units "micromoles per kilogram (umol/kg)";
  }
  bicarbonate {
    Float32 _FillValue NaN;
    Float32 actual_range 1470.431, 1758.909;
    String bcodmo_name "bicarbonate";
    String description "Bicarbonate ion (HCO3-) concentration.";
    String long_name "Bicarbonate";
    String units "micromoles per kilogram (umol/kg)";
  }
  carbonate {
    Float32 _FillValue NaN;
    Float32 actual_range 112.525, 267.699;
    String bcodmo_name "carbonate";
    String description "Carbonate ion (CO3-) concentration.";
    String long_name "Carbonate";
    String units "micromoles per kilogram (umol/kg)";
  }
  omega_Ca {
    Float32 _FillValue NaN;
    Float32 actual_range 2.836, 6.553;
    String bcodmo_name "Calcite Saturation State";
    String description "The saturation state of seawater with respect to calcite.";
    String long_name "Omega Ca";
    String units "dimensionless";
  }
  omega_Ar {
    Float32 _FillValue NaN;
    Float32 actual_range 1.86, 4.365;
    String bcodmo_name "OM_ar";
    String description "The saturation state of seawater with respect to aragonite.";
    String long_name "Omega Ar";
    String units "dimensionless";
  }
  NH4 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.015, 1.884;
    String bcodmo_name "Ammonium";
    Float64 colorBarMaximum 5.0;
    Float64 colorBarMinimum 0.0;
    String description "Ammonium (NH4+) concentration.";
    String long_name "Mole Concentration Of Ammonium In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AMONAAZX/";
    String units "micromolar (uM)";
  }
  PO4 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.009, 5.689;
    String bcodmo_name "PO4";
    String description "Phosphate (PO4---) concentration.";
    String long_name "Mass Concentration Of Phosphate In Sea Water";
    String units "micromolar (uM)";
  }
  NO3_NO2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.015, 1.878;
    String bcodmo_name "NO3_NO2";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Nitrate/nitrite (NO3-/NO2-) concentration.";
    String long_name "Mole Concentration Of Nitrate In Sea Water";
    String units "micromolar (uM)";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Surface water samples (0-3m, n=195) for salinity, nutrients, total alkalinity
(TA), and dissolved inorganic carbon (DIC) were collected from reef sites
throughout the Palauan archipelago at multiple time points between sunrise and
sunset on September 19-24, 2011; March 28-April 7, 2012; December 7-9, 2012;
and November 1-15, 2013 (detailed water sampling procedure in Shamberger et
al. (2014)). Sampling was performed from small boats taken out daily from the
Palau International Coral Reef Center (PICRC).
 
In situ temperature was measured in 2011 with TidbiT v2 water temperature data
loggers produced by Onset with a manufacturer stated accuracy of 0.2 degrees
C, in 2012 with a RBR XR-620 CTD with a manufacturer stated temperature
accuracy of +/-0.002 degrees C, and in 2013 with a Sontek Castaway CTD with a
manufacturer stated temperature accuracy of +/-0.05 degrees C. Surface water
(0-3 m) samples were collected multiple times a day between sunrise and sunset
and on 3-9 separate days for each site from a Niskin bottle into 300 ml glass
bottles (TA/DIC) and 125 ml glass bottles (salinity). Approximately 5 ml were
removed from each bottle to allow headspace for expansion and each TA/DIC
sample was poisoned with 50 ul saturated mercuric chloride solution
immediately after collection to inhibit biological activity and then sealed
with screw tops and tape.
 
TA and DIC analyses were performed using a Versatile Instrument for the
Determination of Total inorganic carbon and titration Alkalinity (VINDTA,
Marianda Analytics and Data), which uses open cell potentiometric (TA) and
coulometric (DIC) titrations, and standardized using certified reference
materials obtained from Andrew Dickson (Scripps Institution of Oceanography;
Dickson 2001, Dickson et al. 2007). Analysis of replicate samples (n=13)
showed a mean precision of ~2 umol/kg for TA and ~1 umol/kg for DIC.";
    String awards_0_award_nid "54896";
    String awards_0_award_number "OCE-1041106";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1041106";
    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 "David L. Garrison";
    String awards_0_program_manager_nid "50534";
    String awards_1_award_nid "520400";
    String awards_1_award_number "OCE-1220529";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward?AWD_ID=1220529";
    String awards_1_funder_name "NSF Division of Ocean Sciences";
    String awards_1_funding_acronym "NSF OCE";
    String awards_1_funding_source_nid "355";
    String awards_1_program_manager "David L. Garrison";
    String awards_1_program_manager_nid "50534";
    String awards_2_award_nid "560427";
    String awards_2_award_number "OCE-1031971";
    String awards_2_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1031971";
    String awards_2_funder_name "NSF Division of Ocean Sciences";
    String awards_2_funding_acronym "NSF OCE";
    String awards_2_funding_source_nid "355";
    String awards_2_program_manager "David L. Garrison";
    String awards_2_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Carbonate chemistry data from 13 coral reef sites in Palau 
 PI: Anne Cohen (WHOI) 
 Co-PIs: D. McCorkle (WHOI), A. Tarrant (WHOI), S. de Putron (BIOS), K. Karnauskas (WHOI) 
 Contact: Hannah Barkley or Anne Cohen (WHOI); K. Shamberger (TAMU) 
 Version History: 
    updated/current version: 23 June 2015 
    original data submitted by K.Shamberger (TAMU): 29 Jan 2014";
    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 2.3  19 Dec 2019";
    String date_created "2014-01-29T20:31:51Z";
    String date_modified "2019-11-14T19:48:11Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.489014.1";
    Float64 Easternmost_Easting 134.557;
    Float64 geospatial_lat_max 7.577;
    Float64 geospatial_lat_min 7.158;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 134.557;
    Float64 geospatial_lon_min 134.349;
    String geospatial_lon_units "degrees_east";
    String history 
"2022-08-16T04:36:04Z (local files)
2022-08-16T04:36:04Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_489014.das";
    String infoUrl "https://www.bco-dmo.org/dataset/489014";
    String institution "BCO-DMO";
    String instruments_0_acronym "Niskin bottle";
    String instruments_0_dataset_instrument_description "Surface water (0-3 m) samples were collected multiple times a day between sunrise and sunset and on 3-9 separate days for each site from a Niskin bottle into 300 ml glass bottles (TA/DIC) and 125 ml glass bottles (salinity).";
    String instruments_0_dataset_instrument_nid "489282";
    String instruments_0_description "A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends.  The bottles can be attached individually on a hydrowire or deployed in 12, 24 or 36 bottle Rosette systems mounted on a frame and combined with a CTD.  Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0412/";
    String instruments_0_instrument_name "Niskin bottle";
    String instruments_0_instrument_nid "413";
    String instruments_0_supplied_name "Niskin bottle";
    String instruments_1_acronym "Water Temp Sensor";
    String instruments_1_dataset_instrument_description 
"In 2011, in situ temperature was measured with TidbiT v2 water temperature data loggers produced by Onset with a manufacturer stated accuracy of 0.2 degrees C. In 2012, in situ temperature was measured with a RBR XR-620 CTD with a manufacturer stated temperature accuracy of +/- 0.002 degrees C.
Information from the manufacturers:TidbiT v2 Temperature Data LoggerRBR XR-620";
    String instruments_1_dataset_instrument_nid "489281";
    String instruments_1_description "General term for an instrument that measures the temperature of the water with which it is in contact (thermometer).";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/134/";
    String instruments_1_instrument_name "Water Temperature Sensor";
    String instruments_1_instrument_nid "647";
    String instruments_1_supplied_name "Water Temperature Sensors";
    String instruments_2_acronym "inorganic carbon and alkalinity analyser";
    String instruments_2_dataset_instrument_description "TA and DIC analyses were performed with a Versatile INstrument for the Determination of Total inorganic carbon and titration Alkalinity (VINDTA) produced by Marianda Marine Analytics and Data. The VINDTA uses coulometric titration for DIC analysis and an open cell potentiometric titration for TA analysis.";
    String instruments_2_dataset_instrument_nid "489284";
    String instruments_2_description "The Versatile INstrument for the Determination of Total inorganic carbon and titration Alkalinity (VINDTA) 3C is a laboratory alkalinity titration system combined with an extraction unit for coulometric titration, which simultaneously determines the alkalinity and dissolved inorganic carbon content of a sample. The sample transport is performed with peristaltic pumps and acid is added to the sample using a membrane pump. No pressurizing system is required and only one gas supply (nitrogen or dry and CO2-free air) is necessary. The system uses a Metrohm Titrino 719S, an ORION-Ross pH electrode and a Metrohm reference electrode. The burette, the pipette and the analysis cell have a water jacket around them. Precision is typically +/- 1 umol/kg for TA and/or DIC in open ocean water.";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0481/";
    String instruments_2_instrument_name "MARIANDA VINDTA 3C total inorganic carbon and titration alkalinity analyser";
    String instruments_2_instrument_nid "686";
    String instruments_2_supplied_name "VINDTA";
    String keywords "altimetry, ammonia, ammonium, bco, bco-dmo, bicarbonate, biological, carbon, carbon dioxide, carbonate, chemical, chemistry, co2, CO2_diss, co3, concentration, data, dataset, date, dic, dioxide, diss, dmo, earth, Earth Science > Oceans > Ocean Chemistry > Ammonia, Earth Science > Oceans > Ocean Chemistry > Nitrate, Earth Science > Oceans > Ocean Chemistry > pH, Earth Science > Oceans > Ocean Chemistry > Phosphate, erddap, laboratory, latitude, longitude, management, mass, mass_concentration_of_phosphate_in_sea_water, mole, mole_concentration_of_ammonium_in_sea_water, mole_concentration_of_nitrate_in_sea_water, n02, name, nh4, nitrate, no3, NO3_NO2, ocean, oceanography, oceans, office, omega, omega_Ar, omega_Ca, pCO2, phosphate, po4, preliminary, reported, sal, satellite, scale, science, sea, sea_water_ph_reported_on_total_scale, seawater, site, site_name, temperature, time, time2, total, water";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/489014/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/489014";
    Float64 Northernmost_Northing 7.577;
    String param_mapping "{'489014': {'lat': 'master - latitude', 'lon': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/489014/parameters";
    String people_0_affiliation "Woods Hole Oceanographic Institution";
    String people_0_affiliation_acronym "WHOI";
    String people_0_person_name "Anne L Cohen";
    String people_0_person_nid "51428";
    String people_0_role "Lead Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Bermuda Institute of Ocean Sciences";
    String people_1_affiliation_acronym "BIOS";
    String people_1_person_name "Samantha J. de Putron";
    String people_1_person_nid "51431";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Woods Hole Oceanographic Institution";
    String people_2_affiliation_acronym "WHOI";
    String people_2_person_name "Kristopher Karnauskas";
    String people_2_person_nid "560431";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI";
    String people_3_person_name "Daniel C McCorkle";
    String people_3_person_nid "51429";
    String people_3_role "Co-Principal Investigator";
    String people_3_role_type "originator";
    String people_4_affiliation "Woods Hole Oceanographic Institution";
    String people_4_affiliation_acronym "WHOI";
    String people_4_person_name "Ann M. Tarrant";
    String people_4_person_nid "51430";
    String people_4_role "Co-Principal Investigator";
    String people_4_role_type "originator";
    String people_5_affiliation "Woods Hole Oceanographic Institution";
    String people_5_affiliation_acronym "WHOI";
    String people_5_person_name "Hannah Barkley";
    String people_5_person_nid "560803";
    String people_5_role "Contact";
    String people_5_role_type "related";
    String people_6_affiliation "Texas A&M University";
    String people_6_affiliation_acronym "TAMU";
    String people_6_person_name "Kathryn E.F. Shamberger";
    String people_6_person_nid "488857";
    String people_6_role "Contact";
    String people_6_role_type "related";
    String people_7_affiliation "Woods Hole Oceanographic Institution";
    String people_7_affiliation_acronym "WHOI BCO-DMO";
    String people_7_person_name "Shannon Rauch";
    String people_7_person_nid "51498";
    String people_7_role "BCO-DMO Data Manager";
    String people_7_role_type "related";
    String project "OA Nutrition and Coral Calcification,Coral Reef Ecosystem OA Impact,Thermal Thresholds and Projections";
    String projects_0_acronym "OA Nutrition and Coral Calcification";
    String projects_0_description 
"The project description is a modification of the original NSF award abstract.
This research project is part of the larger NSF funded CRI-OA collaborative research initiative and was funded as an Ocean Acidification-Category 1, 2010 award. Over the course of this century, all tropical coral reef ecosystems, whether fringing heavily populated coastlines or lining remote islands and atolls, face unprecedented threat from ocean acidification caused by rising levels of atmospheric CO2. In many laboratory experiments conducted to date, calcium carbonate production (calcification) by scleractinian (stony) corals showed an inverse correlation to seawater saturation state OMEGAar), whether OMEGAar was manipulated by acid or CO2 addition. Based on these data, it is predicted that coral calcification rates could decline by up to 80% of modern values by the end of this century. A growing body of new experimental data however, suggests that the coral calcification response to ocean acidification may be less straightforward and a lot more variable than previously recognized. In at least 10 recent experiments including our own, 8 different tropical and temperate species reared under nutritionally-replete but significantly elevated CO2 conditions (780-1200 ppm, OMEAGar ~1.5-2), continued to calcify at rates comparable to conspecifics reared under ambient CO2. These experimental results are consistent with initial field data collected on reefs in the eastern Pacific and southern Oman, where corals today live and accrete their skeletons under conditions equivalent to 2X and 3X pre-industrial CO2. On these high CO2, high nutrient reefs (where nitrate concentrations typically exceed 2.5 micro-molar), coral growth rates rival, and sometimes even exceed, those of conspecifics in low CO2, oligotrophic reef environments.
The investigators propose that a coral's energetic status, tightly coupled to the availability of inorganic nutrients and/or food, is a key factor in the calcification response to CO2-induced ocean acidification. Their hypothesis, if confirmed by the proposed laboratory investigations, implies that predicted changes in coastal and open ocean nutrient concentrations over the course of this century, driven by both climate impacts on ocean stratification and by increased human activity in coastal regions, could play a critical role in exacerbating and in some areas, modulating the coral reef response to ocean acidification. This research program builds on the investigators initial results and observations. The planned laboratory experiments will test the hypothesis that: (1) The coral calcification response to ocean acidification is linked to the energetic status of the coral host. The relative contribution of symbiont photosynthesis and heterotrophic feeding to a coral's energetic status varies amongst species. Enhancing the energetic status of corals reared under high CO2, either by stimulating photosynthesis with inorganic nutrients or by direct heterotrophic feeding of the host lowers the sensitivity of calcification to decreased seawater OMEGAar; (2) A species-specific threshold CO2 level exists over which enhanced energetic status can no longer compensate for decreased OMEGAar of the external seawater. Similarly, we will test the hypothesis that a nutrient threshold exists over which nutrients become detrimental for calcification even under high CO2 conditions; and (3) Temperature-induced reduction of algal symbionts is one stressor that can reduce the energetic reserve of the coral host and exacerbate the calcification response to ocean acidification.
The investigator's initial findings highlight the critical importance of energetic status in the coral calcification response to ocean acidification. Verification of these findings in the laboratory, and identification of nutrient and CO2 thresholds for a range of species will have immediate, direct impact on predictions of reef resilience in a high CO2 world. The research project brings together a diverse group of expertise in coral biogeochemistry, chemical oceanography, molecular biology and coral reproductive ecology to focus on a problem that has enormous societal, economic and conservation relevance.";
    String projects_0_end_date "2013-09";
    String projects_0_geolocation "global; experimental";
    String projects_0_name "An Investigation of the Role of Nutrition in the Coral Calcification Response to Ocean Acidification";
    String projects_0_project_nid "2183";
    String projects_0_start_date "2010-10";
    String projects_1_acronym "Coral Reef Ecosystem OA Impact";
    String projects_1_description 
"text copied from the NSF award abstract: 
Much of our understanding of the impact of ocean acidification on coral reef calcification comes from laboratory manipulation experiments in which reef organisms are removed from their natural habitat and reared under conditions of calcium carbonate saturation (Omega) predicted for the tropical oceans at the end of this century. By comparison, there is a paucity of in situ data describing the sensitivity of coral reef ecosystems to changes in calcium carbonate saturation. Yet emerging evidence suggests there may be critical differences between the calcification response of organisms in culture and the net calcification response of a coral reef ecosystem, to the same degree of change in calcium carbonate saturation. In the majority of cases, the sensitivity of net reef calcification to changing calcium carbonate saturation is more severe than laboratory manipulation experiments predict. Clearly, accurate predictions of the response of coral reef ecosystems to 21st century ocean acidification will depend on a robust characterization of ecosystem-scale responses and an understanding of the fundamental processes that shape them. Using existing data, the investigators show that the sensitivity of coral reef ecosystem calcification to Delta calcium carbonate saturation conforms to the empirical rate equation R=k(Aragonite saturation state -1)n, which also describes the relationship between the rate of net abiogenic CaCO3 precipitation (R) and the degree of Aragonite supersaturation (Aragonite saturation state-1). By implication, the net ecosystem calcification (NEC) response to ocean acidification is governed by fundamental laws of physical chemistry and is potentially predictable across space and time. When viewed this way, the existing, albeit sparse, dataset of NEC reveals distinct patterns that, if verified, have important implications for how different coral reef ecosystems will respond to 21st century ocean acidification. The investigators have outlined a research program designed to build on this proposition. The project expands the currently sparse dataset of ecosystem-scale observations at four strategically placed reef sites: 2 sites in the Republic of Palau, Caroline Islands, Micronesia, western Pacific Ocean; a third at Dongsha Atoll, Pratas Islands, South China Sea; and the fourth at Kingman Reef, US Northern Line Islands, 6 deg. 23 N, 162 deg. 25 W.  The four selected sites will allow investigators to test the following hypotheses: (1) The sensitivity (\"n\" in the rate equation) of coral reef ecosystem calcification to Delta Aragonite saturation state decreases with decreasing Aragonite saturation state. By implication, the rate at which reef calcification declines will slow as ocean acidification progresses over the course of this century. (2) The energetic status of the calcifying community is a key determinant of absolute rates of net ecosystem calcification (\"k\" in the rate equation), which, combined with n, defines the Aragonite saturation state value at which NEC approaches zero. By implication, the shift from net calcification to net dissolution will be delayed in healthy, energetically replete coral reef ecosystems and accelerated in perturbed, energetically depleted ecosystems. and (3) The calcification response of individual colonies of dominant reef calcifiers (corals and algae) is weaker than the measured ecosystem-scale response to the same change in Aragonite saturation state. By implication, processes not adequately captured in laboratory experiments, such as bioerosion and dissolution, will play an important role in the coral reef response to ocean acidification.
Broader Impacts: Ocean acidification threatens the livelihoods of 500 million people worldwide who depend on coral reefs to provide habitable and agricultural land, food, building materials, coastal protection and income from tourism. Yet data emerging from ocean acidification (OA) studies point to critical gaps in our knowledge of reef ecosystem-scale responses to OA that currently limit our ability to predict the timing and severity of its impact on different reefs in different parts of the world. Using existing data generated by the investigators and others, this project will address a series of related hypotheses, which, if verified by the research, will have an immediate, direct impact on predictions of coral reef resilience in a high CO2 world. This project brings together expertise in coral reef biogeochemistry, chemical oceanography and physical oceanography to focus on a problem that has enormous societal, economic and conservation relevance. In addition to sharing the resultant data via BCO-DMO, project data will also be contributed to the Ocean Acidification International Coordination Centre (OA-ICC) data collection hosted at the PANGAEA Open Access library (http://www.pangaea.de).";
    String projects_1_end_date "2015-08";
    String projects_1_geolocation "Republic of Palau, Caroline Islands, Micronesia, western Pacific Ocean; Dongsha Atoll, Pratas Islands, South China Sea; Kingman Reef, US Northern Line Islands, 6 deg. 23 N, 162 deg. 25 W";
    String projects_1_name "Toward Predicting the Impact of Ocean Acidification on Net Calcification by a Broad Range of Coral Reef Ecosystems: Identifying Patterns and Underlying Causes";
    String projects_1_project_nid "520413";
    String projects_1_start_date "2012-09";
    String projects_2_acronym "Thermal Thresholds and Projections";
    String projects_2_description 
"Description from NSF award abstract:
Sea surface temperature (SST) across much of the global tropics has increased by 0.5-1 degrees C in the past 4 decades and, with it, the frequency and geographic extent of coral bleaching events and reef mortality. As levels of atmospheric CO2 continue to rise, there is mounting concern that CO2-induced climate change will pose the single greatest threat to the survival of coral reefs. Averaged output of 21 IPCC climate models for a mid-range CO2 emissions scenario predicts that tropical SSTs will increase another 1.5-3 degrees C by the end of this century. Combined with current estimates of thermal thresholds for coral bleaching, the outlook for the future of coral-reef ecosystems, worldwide, appears bleak. There are several key issues that limit accurate predictions of the full and lasting impact of rising SSTs. These include (1) level of confidence in the spatial and temporal patterns of the predicted warming, (2) knowledge of thermal thresholds of different reef-building coral species, and (3) the potential for corals to increase resistance to thermal stress through repeated exposure to high temperature events.
New skeletal markers have been developed that constrain the thermal thresholds and adaptive potential of multiple, individual coral colonies across 3-D space and through time. The method, based on 3-D CAT scan reconstructions of coral skeletons, has generated initial data from two coral species in the Red Sea, Great Barrier Reef and Phoenix Islands. Results showed that large, abrupt declines in skeletal growth occur at thresholds of accumulated heat stress defined by NOAA's Degree Heating Weeks Index (DHWs). In addition, there was a significant correlation between host lipid reserve, an independent measure of stress and mortality risk, and rates of skeletal growth. Because the coral skeleton archives the history of each coral's response to and recovery from successive, documented thermal anomalies, this approach pinpoints the thermal thresholds for sub-lethal impacts, the recovery time (if any) following a return to normal oceanographic conditions, and tests for a dampened response following successive events, indicative of acclimation.
This research program builds on initial work, focusing on method refinement and application to corals on two central Pacific reefs. With contrasting thermal histories, these reefs are considered at greatest risk from future ocean warming. In parallel, new experiments will be run on an ocean general-circulation model (OGCM) that is well suited to the tropical Pacific and of sufficiently high resolution, both horizontal and vertical, to maximize projections of thermal stress on specific central Pacific Reef sites over the next few decades. The OGCM output will also be of sufficient temporal resolution to compute DHWs, thus addressing a major limitation of the direct application of global climate model output (as archived for the IPCC AR4) toward coral-reef studies. Specifically, this study will: (1) collect multiple new, medium-length (15-30 yrs) cores and branches from two dominant reef-building species at 1-30m depth in the Gilbert and Jarvis Islands, central tropical Pacific; (2) apply 3-D CAT scanning and image analysis techniques to quantify systematically thermal thresholds, rates of recovery and resilience for each species, at each reef site and with depth; (3) quantify energetic reserve and symbiont genotype amongst thermally more- and less- resilient colonies, establishing a quantitative link between calcification stress and mortality risk, and determining the physiological basis for calcification responses to thermal stress; (4) use an OGCM specifically tailored to the tropical Pacific to produce a dynamically consistent set of forecasts for near-term climate change at the target reef sites; and (5) combine coral data with model output and refine the projected thermal stress forecast, in degree heating weeks, for corals in this central Pacific Island group over the 21st century.";
    String projects_2_end_date "2014-09";
    String projects_2_name "Constraining Thermal Thresholds and Projections of Temperature Stress on Pacific Coral Reefs Over the 21st Century: Method Refinement and Application";
    String projects_2_project_nid "560428";
    String projects_2_start_date "2010-10";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 7.158;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Seawater carbonate chemistry from 13 sites in Palau collected from small boats in the Palauan archipelago from 2011-2013.";
    String title "Seawater carbonate chemistry from 13 sites in Palau collected from small boats in the Palauan archipelago from 2011-2013";
    String version "1";
    Float64 Westernmost_Easting 134.349;
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

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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