Accessing BCO-DMO data
Brought to you by BCO-DMO
|
|
BCO-DMO ERDDAP
Accessing BCO-DMO data |
log in
Brought to you by BCO-DMO |
| Dataset Title: | Sediment trap flux measurements from the Hawaii Ocean Time-Series (HOT) project at station ALOHA. 
|
| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_737393) |
| Information: | Summary
| License
| ISO 19115
| Metadata
| Background
| Subset
| Files
| Make a graph
|
Attributes {
s {
Cruise {
Int16 _FillValue 32767;
Int16 actual_range 2, 287;
String bcodmo_name "cruise_id";
String description "Cruise Number";
String long_name "Cruise";
String units "unitless";
}
P_flux_filename {
String bcodmo_name "file_name";
String description "Original filename of the particle flux data from HOT";
String long_name "P Flux Filename";
String units "unitless";
}
longitude {
String _CoordinateAxisType "Lon";
Float64 _FillValue NaN;
Float64 actual_range -158.0, -158.0;
String axis "X";
String bcodmo_name "longitude";
Float64 colorBarMaximum 180.0;
Float64 colorBarMinimum -180.0;
String description "Longitude with East negative";
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";
}
latitude {
String _CoordinateAxisType "Lat";
Float64 _FillValue NaN;
Float64 actual_range 22.75, 22.75;
String axis "Y";
String bcodmo_name "latitude";
Float64 colorBarMaximum 90.0;
Float64 colorBarMinimum -90.0;
String description "Latitude with South negative";
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";
}
depth {
String _CoordinateAxisType "Height";
String _CoordinateZisPositive "down";
Float64 _FillValue NaN;
Float64 actual_range 70.0, 500.0;
String axis "Z";
String bcodmo_name "depth";
Float64 colorBarMaximum 8000.0;
Float64 colorBarMinimum -8000.0;
String colorBarPalette "TopographyDepth";
String description "Depth";
String ioos_category "Location";
String long_name "Depth";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
String positive "down";
String standard_name "depth";
String units "m";
}
Treatment {
String bcodmo_name "treatment";
String description "C-Solutions from individual traps combined and replicate subsamples drawn from this solution. I-Individual traps sampled as replicates. W-Swimmers picked out before analyzed.O-Some other (special?) treatment.";
String long_name "Treatment";
String units "unitless";
}
Carbon {
Float32 _FillValue NaN;
Float32 actual_range 3.5, 61.4;
String bcodmo_name "C";
String description "Carbon";
String long_name "Carbon";
String units "miligrams per square meter per day (mg/m2/d)";
}
Carbon_sd_diff {
Float32 _FillValue NaN;
Float32 actual_range 0.1, 28.6;
String bcodmo_name "C";
Float64 colorBarMaximum 50.0;
Float64 colorBarMinimum 0.0;
String description "Standard Deviation presented where Carbon_n=3; Difference between replicate presented where Carbon_n=2";
String long_name "Carbon Sd Diff";
String units "miligrams per square meter per day (mg/m2/d)";
}
Carbon_n {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "replicate";
String description "Number of replicate samples collected for replicate analysis.";
String long_name "Carbon N";
String units "unitless";
}
Nitrogen {
Float32 _FillValue NaN;
Float32 actual_range 0.27, 14.3;
String bcodmo_name "N";
String description "Nitrogen";
String long_name "Nitrogen";
String units "miligrams per square meter per day (mg/m2/d)";
}
Nitrogen_sd_diff {
Float32 _FillValue NaN;
Float32 actual_range 0.03, 3.84;
String bcodmo_name "N";
Float64 colorBarMaximum 50.0;
Float64 colorBarMinimum 0.0;
String description "Standard Deviation presented where Nitrogen_n=3; Difference between replicate presented where Nitrogen_n=2";
String long_name "Nitrogen Sd Diff";
String units "miligrams per square meter per day (mg/m2/d)";
}
Nitrogen_n {
Byte _FillValue 127;
Byte actual_range 1, 9;
String bcodmo_name "replicate";
String description "Number of replicate samples collected for replicate analysis.";
String long_name "Nitrogen N";
String units "unitless";
}
Phosphorus {
Float32 _FillValue NaN;
Float32 actual_range 0.008, 1.136;
String bcodmo_name "PIP";
String description
"Phosphorus
Addendum - PPO4 protocol (April 7 2015) The method used for the analysis of particulate phosphate (PPO4) has been modified and applied to samples analyzed November 2011 (HOT 236) to the present. The previous protocol was in use over at least the previous 10-year period. The modified procedure included vortexing of the sample prior to a longer leaching time (1 hour versus 30 min) of the GFF filter in 0.15 N HCl at room temperature. Both the previous and modified procedures were tested in paired analyses on samples collected over one year (12 cruises). The modified procedure resulted in higher yields by approximately 50% for water column samples (integrated 0-100 m: old method 1.00±0.27 mmol P m-2 versus 1.56±0.14 mmol P m-2) and approximately 30% for P-flux estimated from sediment trap samples (old method: 0.31±0.07 mg P m-2 d-1 versus 0.40±0.09 mg P m-2 d-1). Please see the HOT Data Report 2012 for more detail.";
String long_name "Phosphorus";
String units "miligrams per square meter per day (mg/m2/d)";
}
Phosphorus_sd_diff {
Float32 _FillValue NaN;
Float32 actual_range 0.001, 0.366;
String bcodmo_name "PIP";
Float64 colorBarMaximum 50.0;
Float64 colorBarMinimum 0.0;
String description "Standard Deviation presented where Phosphorus_n=3; Difference between replicate presented where Phosphorus_n=2";
String long_name "Phosphorus Sd Diff";
String units "miligrams per square meter per day (mg/m2/d)";
}
Phosphorus_n {
Byte _FillValue 127;
Byte actual_range 1, 3;
String bcodmo_name "replicate";
String description "Number of replicate samples collected for replicate analysis.";
String long_name "Phosphorus N";
String units "unitless";
}
Mass {
Float32 _FillValue NaN;
Float32 actual_range 8.5, 131.0;
String bcodmo_name "mass";
String description "Mass";
String long_name "Mass";
String units "miligrams per square meter per day (mg/m2/d)";
}
Mass_sd_diff {
Float32 _FillValue NaN;
Float32 actual_range 0.3, 49.1;
String bcodmo_name "mass";
Float64 colorBarMaximum 50.0;
Float64 colorBarMinimum 0.0;
String description "Standard Deviation presented where Mass_n=3; Difference between replicate presented where Mass_n=2";
String long_name "Mass Sd Diff";
String units "miligrams per square meter per day (mg/m2/d)";
}
Mass_n {
Byte _FillValue 127;
Byte actual_range 2, 8;
String bcodmo_name "replicate";
String description "Number of replicate samples collected for replicate analysis.";
String long_name "Mass N";
String units "unitless";
}
Silica {
Float32 _FillValue NaN;
Float32 actual_range 0.189, 21.579;
String bcodmo_name "Si";
String description "Silica";
String long_name "Silica";
String units "miligrams per square meter per day (mg/m2/d)";
}
Silica_sd_diff {
Float32 _FillValue NaN;
Float32 actual_range 0.03, 7.367;
String bcodmo_name "Si";
Float64 colorBarMaximum 50.0;
Float64 colorBarMinimum 0.0;
String description "Standard Deviation presented where Silica_n=3; Difference between replicate presented where Silica_n=2";
String long_name "Silica Sd Diff";
String units "miligrams per square meter per day (mg/m2/d)";
}
Silica_n {
Byte _FillValue 127;
Byte actual_range 2, 3;
String bcodmo_name "replicate";
String description "Number of replicate samples collected for replicate analysis.";
String long_name "Silica N";
String units "unitless";
}
Delta_15N {
Float32 _FillValue NaN;
Float32 actual_range -1.17, 6.89;
String bcodmo_name "d15N";
String description "Delta-15N of PN (permil vs. air-N2)";
String long_name "Delta 15 N";
String units "permil vs. air-N2";
}
Delta_15N_sd_diff {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 1.93;
String bcodmo_name "d15N";
Float64 colorBarMaximum 50.0;
Float64 colorBarMinimum 0.0;
String description "Standard Deviation presented where Delta_15N_n=3; Difference between replicate presented where Delta_15N_n=2";
String long_name "Delta 15 N Sd Diff";
String units "miligrams per square meter per day (mg/m2/d)";
}
Delta_15N_n {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "replicate";
String description "Number of replicate samples collected for replicate analysis.";
String long_name "Delta 15 N N";
String units "unitless";
}
Delta_13C {
Float32 _FillValue NaN;
Float32 actual_range -27.5, -16.96;
String bcodmo_name "delta13C";
String description "Delta-13C of PC (permil vs. VPDB)";
String long_name "Delta 13 C";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/D13CMITX/";
String units "permil vs. VPDB";
}
Delta_13C_sd_diff {
Float32 _FillValue NaN;
Float32 actual_range 0.05, 1.94;
String bcodmo_name "delta13C";
Float64 colorBarMaximum 50.0;
Float64 colorBarMinimum 0.0;
String description "Standard Deviation presented where Delta_13C_n=3; Difference between replicate presented where Delta_13C_n=2";
String long_name "Delta 13 C Sd Diff";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/D13CMITX/";
String units "miligrams per square meter per day (mg/m2/d)";
}
Delta_13C_n {
Byte _FillValue 127;
Byte actual_range 1, 6;
String bcodmo_name "replicate";
String description "Number of replicate samples collected for replicate analysis.";
String long_name "Delta 13 C N";
String units "unitless";
}
PIC {
Float32 _FillValue NaN;
Float32 actual_range 0.04, 9.63;
String bcodmo_name "PIC";
String description "Particulate Inorganic Carbon";
String long_name "Particulate Inorganic Carbon";
String units "miligrams per square meter per day (mg/m2/d)";
}
PIC_sd_diff {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 4.56;
String bcodmo_name "PIC";
Float64 colorBarMaximum 50.0;
Float64 colorBarMinimum 0.0;
String description "Standard Deviation presented where PIC_n=3; Difference between replicate presented where PIC_n=2";
String long_name "PIC Sd Diff";
String units "miligrams per square meter per day (mg/m2/d)";
}
PIC_n {
Byte _FillValue 127;
Byte actual_range 1, 3;
String bcodmo_name "replicate";
String description "Number of replicate samples collected for replicate analysis.";
String long_name "PIC N";
String units "unitless";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
String acquisition_description
"Particle flux was measured at a standard reference depth of 150 m using
multiple cylindrical particle interceptor traps deployed on a free-floating
array for approximately 60 h during each cruise. Sediment trap design and
collection methods are described in Winn et al. (1991). Samples were analyzed
for particulate C, N, P & Si. Typically six traps are analyzed for PC and PN,
three for PP, and another three traps for PSi.
The information below has been copied from the HOT Field & Laboratory
Protocols page, found at
[http://hahana.soest.hawaii.edu/hot/protocols/protocols.html#](\\\\\"http://hahana.soest.hawaii.edu/hot/protocols/protocols.html#\\\\\")
(last visited on 2018-05-23).
SUMMARY: Passively sinking particulate matter is collected using a free-
floating sediment array and, after prescreening (335 \\u00b5m) to remove
zooplankton and micronekton carcasses, the sample materials are analyzed for
C, N, P and mass flux (mg m-2 d-1).
1\\. Principle
Although most of the particulate matter both on the seafloor and in
suspension in seawater is very fine, recent evidence suggests that most of the
material deposited on the benthos arrives via relatively rare, rapidly sinking
large particles (McCave, 1975). Therefore, in order to describe adequately the
ambient particle field and to understand the rates and mechanisms of
biogeochemical cycling in the marine environment, it is imperative to employ
sampling methods that enable the investigator to distinguish between the
suspended and sinking pools of particulate matter. This universal requirement
for a careful and comprehensive analysis of sedimenting particles has resulted
in the development, evaluation and calibration of a variety of in situ
particle collectors or sediment traps. The results, after nearly a decade of
intensive field experiments, have contributed significantly to our general
understanding of: (1) the relationship between the rate of primary production
and downward flux of particulate organic matter, (2) mesopelagic zone oxygen
consumption and nutrient regeneration, (3) biological control of the removal
of abiogenic particles from the surface ocean and (4) seasonal and interannual
variations in particle flux to the deep-sea. Future sediment trap studies
will, most likely, continue to provide novel and useful data on the rates and
mechanisms of important biogeochemical processes.
At Station ALOHA, we presently deploy a free-drifting sediment trap array
with 12 individual collectors positioned at 150, 300 and 500 m. The deployment
period is generally 72 hours. The passively sinking particles are subsequently
analyzed for a variety of chemical properties, including: total mass, C, N and
P.
2. Precautions
Because particle fluxes in oligotrophic habitats are expected to be low,
special attention must be paid to the preparation of individual sediment trap
collector tubes so that they are clean and free of dust and other potentially
contaminating particles. Traps should be capped immediately after filling and
immediately after retrieval. Pay particular attention to airborne and/or
shipboard particulate contamination sources. In addition, the time interval
between trap retrieval and subsample filtration should be minimized in order
to limit the inclusion of extraneous abiotic particles and the post-collection
solubilization of particles.
3. Field Operations
3.1.
Hardware
Our free-floating sediment trap array is patterned after the MULTITRAP
system pioneered by Knauer et al. (1979) and used extensively in the decade-
long VERTEX program. Twelve individual sediment trap collectors (0.0039 m2)
are typically deployed at three depths (150, 300 and 500 m). The traps are
affixed to a PVC cross attached to 1/2\\\" polypropylene line. The traps are
tracked using VHF radio and Argos satellite transmitters and strobelights.
Typically we deploy our traps for a period of 72 hours each cruise.
3.2.
Trap solutions
Prior to deployment, each trap is cleaned with 1 M HCl, rinsed thoroughly
with deionized water then filled with a high density solution to prevent
advective-diffusive loss of extractants and preservatives during the
deployment period and to eliminate flushing of the traps during recovery
(Knauer et al., 1979). The trap solution is prepared by adding 50 g of NaCl to
each liter of surface seawater. This brine solution is pressure filtered
sequentially through a 1.0 and 0.5 \\u00b5m filter cartridge prior to the
addition of 10 ml 100% formalin l-1. Individual traps are filled and at least
10 l of the trap solution is saved for analysis of solution blanks (see
sections 4.1 and 5.1).
3.3.
Post-recovery processing
3.3.1.
Upon recovery, individual traps are capped and transported to the shipboard
portable laboratory for analysis. Care is taken not to mix the higher density
trap solutions with the overlying seawater. Trap samples are processed from
deep to shallow in order to minimize potential contamination.
3.3.2.
The depth of the interface between the high density solution and overlying
seawater is marked on each trap. The overlying seawater is then aspirated with
a plastic tube attached to a vacuum system in order to avoid disturbing the
high density solution. Because some sinking particulate material collects near
the interface between the high density solution and the overlying seawater,
the overlying seawater is removed only to a depth that is 5 cm above the
previously identified interface.
3.3.3.
After the overlying seawater has been removed from all the traps at a given
depth, the contents of each trap is passed through an acid rinsed 335 \\u00b5m
NitexR screen to remove contaminating zooplankton and micronekton which
entered the traps in a living state and are not truly part of the passive
flux. Immediately before this sieving process, the contents of each trap are
mixed to disrupt large amorphous particles. The traps are rinsed with a
portion of the <335 \\u00b5m sample in order to recover all particulate matter,
and the 335 \\u00b5m NitexR screen is examined to determine whether residual
material, in addition to the so-called \\\"swimmers\\\", is present. If so, the
screens are rinsed again with a portion of the 335 \\u00b5m filtrate. After all
traps from a given depth have been processed, the 335 \\u00b5m screen is
removed and placed into a vial containing 20 ml of formalin- seawater
solution, and stored at 4 \\u00b0C for subsequent microscopic examination and
organism identification and enumeration.
4. Determination of Mass Flux
4.1.
Three of the 12 traps deployed at each water depth are used for the
determination of mass flux. At our shore-based laboratory, triplicate 250 ml
subsamples of the time-zero high density trap solution (blank) and equivalent
volumes individual traps (start with the deepest depth and work up), are
vacuum filtered through tared 25 mm 0.2 \\u00b5m Nuclepore membrane filters
(see Chapter 18, sections 4.1.4 to 4.1.3). The tared filters are prepared as
follows:
4.1.1.
Rinse filters three times with distilled water. Place rinsed filter on a 2.5
cm2 foil square (to reduce static electricity) in a plastic 47 mm petri dish.
4.1.2.
Fold the foil in half over the filter and place the petri dish in a drying
oven with the lid ajar for 2 hours at 55 \\u00b0C. Remove and cool in
dessicator for 30 minutes.
4.1.3.
Weigh filter to constant weight (i.e., repeat oven drying, cooling and
weighing until a relative standard deviation of <0.005% is achieved), on a
microbalance capable of 0.1 \\u00b5g resolution. Record weights (to the nearest
0.1 \\u00b5g) on label tape placed on top of the petri dish.
4.2.
After the last of the sample has passed through the filter, the walls of the
filter funnel are washed with three consecutive 5 ml rinses of an isotonic (1
M) ammonium formate solution to remove seawater salts. During each rinse,
allow the ammonium formate solution to completely cover the filter.
4.3.
Return the processed filter to its petri dish, record sample number (on the
dish and data sheet), and place in a drying oven at 55 \\u00b0C for 8 hours.
Alternately, store in a dessicator, if an oven is not immediately available.
Dry to constant weight (as in Chapter 18, section 4.1.3).
5\\. Determination of C, N and P Flux
5.1.
The quantities of particulate C, N and P in the prescreened trap solutions
are determined using methods described in Chapters 10 and 11. Six replicate
traps are used for C/N determinations and three additional traps for P.
Typically, 1.5-2 liters are used for a single C/N or P measurement. An
equivalent volume of the time-zero sediment trap solution, filtered through
the appropriate filters is used as a C, N or P blank
Addendum - PPO4 protocol (April 7, 2015)
The method used for the analysis of particulate phosphate (PPO4) has been
modified
and applied to samples analyzed November 2011 (HOT 236) to the present. The
previous
protocol was in use over at least the previous 10-year period.
The modified procedure included vortexing of the sample prior to a longer
leaching
time (1 hour versus 30 min) of the GFF filter in 0.15 N HCl at room
temperature.
Both the previous and modified procedures were tested in paired analyses on
samples
collected over one year (12 cruises). The modified procedure resulted in
higher yields
by approximately 50% for water column samples (integrated 0-100 m: old
method 1.00\\u00b10.27
mmol P m-2, versus 1.56\\u00b10.14 mmol P m-2) and approximately 30% for
P-flux estimated
from sediment trap samples (old method: 0.31\\u00b10.07 mg P m-2 d-1 versus
0.40\\u00b10.09 mg P m-2 d-1).
Please see the HOT Data Report 2012 for more detail";
String awards_0_award_nid "54915";
String awards_0_award_number "OCE-0926766";
String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0926766";
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 cdm_data_type "Other";
String comment
"version: 2018-04-25
Particle flux data
from monthly HOT cruises to deep-water Station ALOHA";
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 "2018-05-23T15:02:45Z";
String date_modified "2019-12-11T14:43:51Z";
String defaultDataQuery "&time<now";
String doi "10.1575/1912/bco-dmo.737393.1";
Float64 Easternmost_Easting -158.0;
Float64 geospatial_lat_max 22.75;
Float64 geospatial_lat_min 22.75;
String geospatial_lat_units "degrees_north";
Float64 geospatial_lon_max -158.0;
Float64 geospatial_lon_min -158.0;
String geospatial_lon_units "degrees_east";
Float64 geospatial_vertical_max 500.0;
Float64 geospatial_vertical_min 70.0;
String geospatial_vertical_positive "down";
String geospatial_vertical_units "m";
String history
"2024-04-19T12:01:32Z (local files)
2024-04-19T12:01:32Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_737393.html";
String infoUrl "https://www.bco-dmo.org/dataset/737393";
String institution "BCO-DMO";
String instruments_0_acronym "Sediment Trap";
String instruments_0_dataset_instrument_description "sediment trap array (spar buoy, radiotransmitter, strobe light, floats, trap supports, collector tubes)";
String instruments_0_dataset_instrument_nid "737470";
String instruments_0_description "Sediment traps are specially designed containers deployed in the water column for periods of time to collect particles from the water column falling toward the sea floor. In general a sediment trap has a jar at the bottom to collect the sample and a broad funnel-shaped opening at the top with baffles to keep out very large objects and help prevent the funnel from clogging. This designation is used when the specific type of sediment trap was not specified by the contributing investigator.";
String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/33/";
String instruments_0_instrument_name "Sediment Trap";
String instruments_0_instrument_nid "518";
String instruments_0_supplied_name "sediment trap array";
String instruments_1_acronym "CHN";
String instruments_1_dataset_instrument_description "PE-2400 Carbon/Nitrogen analyzer with integrator";
String instruments_1_dataset_instrument_nid "737472";
String instruments_1_description "A unit that accurately determines the carbon and nitrogen concentrations of organic compounds typically by detecting and measuring their combustion products (CO2 and NO).";
String instruments_1_instrument_name "Particulate Organic Carbon/Nitrogen Analyzer";
String instruments_1_instrument_nid "654";
String instruments_1_supplied_name "PE-2400 Carbon/Nitrogen analyzer with integrator";
String instruments_2_acronym "Spectrophotometer";
String instruments_2_dataset_instrument_description "spectrophotometer (Perkin-Elmer Lambda 3B) and 1-cm cuvette";
String instruments_2_dataset_instrument_nid "737473";
String instruments_2_description "An instrument used to measure the relative absorption of electromagnetic radiation of different wavelengths in the near infra-red, visible and ultraviolet wavebands by samples.";
String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB20/";
String instruments_2_instrument_name "Spectrophotometer";
String instruments_2_instrument_nid "707";
String instruments_2_supplied_name "spectrophotometer and 1-cm cuvette";
String instruments_3_acronym "Scale";
String instruments_3_dataset_instrument_description "Cahn electronic microbalance";
String instruments_3_dataset_instrument_nid "737471";
String instruments_3_description "An instrument used to measure weight or mass.";
String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB13/";
String instruments_3_instrument_name "Scale";
String instruments_3_instrument_nid "714";
String instruments_3_supplied_name "Cahn electronic microbalance";
String keywords "bco, bco-dmo, biological, carbon, Carbon_n, Carbon_sd_diff, chemical, cruise, data, dataset, delta, Delta_13C, Delta_13C_n, Delta_13C_sd_diff, Delta_15N, Delta_15N_n, Delta_15N_sd_diff, depth, diff, dmo, erddap, filename, flux, inorganic, latitude, longitude, management, mass, Mass_n, Mass_sd_diff, nitrogen, Nitrogen_n, Nitrogen_sd_diff, oceanography, office, P_flux_filename, particulate, phosphorus, Phosphorus_n, Phosphorus_sd_diff, pic, PIC_n, PIC_sd_diff, preliminary, silica, Silica_n, Silica_sd_diff, treatment";
String license "https://www.bco-dmo.org/dataset/737393/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/737393";
Float64 Northernmost_Northing 22.75;
String param_mapping "{'737393': {'lat': 'flag - latitude', 'Depth': 'flag - depth', 'lon': 'flag - longitude'}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/737393/parameters";
String people_0_affiliation "University of Hawaii at Manoa";
String people_0_affiliation_acronym "SOEST";
String people_0_person_name "David M. Karl";
String people_0_person_nid "50750";
String people_0_role "Principal Investigator";
String people_0_role_type "originator";
String people_1_affiliation "University of Hawaii at Manoa";
String people_1_affiliation_acronym "SOEST";
String people_1_person_name "Lance A Fujieki";
String people_1_person_nid "51683";
String people_1_role "Contact";
String people_1_role_type "related";
String people_2_affiliation "Woods Hole Oceanographic Institution";
String people_2_affiliation_acronym "WHOI BCO-DMO";
String people_2_person_name "Mathew Biddle";
String people_2_person_nid "708682";
String people_2_role "BCO-DMO Data Manager";
String people_2_role_type "related";
String project "HOT";
String projects_0_acronym "HOT";
String projects_0_description
"Systematic, long-term observations are essential for evaluating natural variability of Earth’s climate and ecosystems and their responses to anthropogenic disturbances. Since October 1988, the Hawaii Ocean Time-series (HOT) program has investigated temporal dynamics in biology, physics, and chemistry at Stn. ALOHA (22°45' N, 158°W), a deep ocean field site in the oligotrophic North Pacific Subtropical Gyre (NPSG). HOT conducts near monthly ship-based sampling and makes continuous observations from moored instruments to document and study NPSG climate and ecosystem variability over semi-diurnal to decadal time scales. HOT was founded to understand the processes controlling the time-varying fluxes of carbon and associated biogenic elements in the ocean and to document changes in the physical structure of the water column. To achieve these broad objectives, the program has several specific goals:
Quantify time-varying (seasonal to decadal) changes in reservoirs and fluxes of carbon (C) and associated bioelements (nitrogen, oxygen, phosphorus, and silicon).
Identify processes controlling air-sea C exchange, rates of C transformation through the planktonic food web, and fluxes of C into the ocean’s interior.
Develop a climatology of hydrographic and biogeochemical dynamics from which to form a multi-decadal baseline from which to decipher natural and anthropogenic influences on the NPSG ecosystem.
Provide scientific and logistical support to ancillary programs that benefit from the temporal context, interdisciplinary science, and regular access to the open sea afforded by HOT program occupation of Sta. ALOHA, including projects implementing, testing, and validating new methodologies, models, and transformative ocean sampling technologies.
Over the past 24+ years, time-series research at Station ALOHA has provided an unprecedented view of temporal variability in NPSG climate and ecosystem processes. Foremost among HOT accomplishments are an increased understanding of the sensitivity of bioelemental cycling to large scale ocean-climate interactions, improved quantification of reservoirs and time varying fluxes of carbon, identification of the importance of the hydrological cycle and its influence on upper ocean biogeochemistry, and the creation of long-term data sets from which the oceanic response to anthropogenic perturbation of elemental cycles may be gauged.
A defining characteristic of the NPSG is the perennially oligotrophic nature of the upper ocean waters. This biogeochemically reactive layer of the ocean is where air-sea exchange of climate reactive gases occurs, solar radiation fuels rapid biological transformation of nutrient elements, and diverse assemblages of planktonic organisms comprise the majority of living biomass and sustain productivity. The prevailing Ekman convergence and weak seasonality in surface light flux, combined with relatively mild subtropical weather and persistent stratification, result in a nutrient depleted upper ocean habitat. The resulting dearth of bioessential nutrients limits plankton standing stocks and maintains a deep (175 m) euphotic zone. Despite the oligotrophic state of the NPSG, estimates of net organic matter production at Sta. ALOHA are estimated to range ~1.4 and 4.2 mol C m2 yr1. Such respectable rates of productivity have highlighted the need to identify processes supplying growth limiting nutrients to the upper ocean. Over the lifetime of HOT numerous ancillary science projects have leveraged HOT science and infrastructure to examine possible sources of nutrients supporting plankton productivity. Both physical (mixing, upwelling) and biotic (N2 fixation, vertical migration) processes supply nutrients to the upper ocean in this region, and HOT has been instrumental in demonstrating that these processes are sensitive to variability in ocean climate.
Station ALOHA - site selection and infrastructure
Station ALOHA is a deep water (~4800 m) location approximately 100 km north of the Hawaiian Island of Oahu. Thus, the region is far enough from land to be free of coastal ocean dynamics and terrestrial inputs, but close enough to a major port (Honolulu) to make relatively short duration (45 m depth), below depths of detection by Earth-orbiting satellites. The emerging data emphasize the value of in situ measurements for validating remote and autonomous detection of plankton biomass and productivity and demonstrate that detection of potential secular-scale changes in productivity against the backdrop of significant interannual and decadal fluctuations demands a sustained sampling effort.
Careful long-term measurements at Stn. ALOHA also highlight a well-resolved, though relatively weak, seasonal climatology in upper ocean primary productivity. Measurements of 14C-primary production document a ~3-fold increase during the summer months (Karl et al., 2012) that coincides with increases in plankton biomass (Landry et al., 2001; Sheridan and Landry, 2004). Moreover, phytoplankton blooms, often large enough to be detected by ocean color satellites, are a recurrent summertime feature of these waters (White et al., 2007; Dore et al., 2008; Fong et al., 2008). Analyses of ~13-years (1992-2004) of particulate C, N, P, and biogenic Si fluxes collected from bottom-moored deep-ocean (2800 m and 4000 m) sediment traps provide clues to processes underlying these seasonal changes. Unlike the gradual summertime increase in sinking particle flux observed in the upper ocean (150 m) traps, the deep sea particle flux record depicts a sharply defined summer maximum that accounts for ~20% of the annual POC flux to the deep sea, and appears driven by rapidly sinking diatom biomass (Karl et al., 2012). Analyses of the 15N isotopic signatures associated with sinking particles at Sta. ALOHA, together with genetic analyses of N2 fixing microorganisms, implicates upper ocean N2 fixation as a major control on the magnitude and efficiency of the biological carbon pump in this ecosystem (Dore et al., 2002; Church et al., 2009; Karl et al., 2012).
Motivating Questions
Science results from HOT continue to raise new, important questions about linkages between ocean climate and biogeochemistry that remain at the core of contemporary oceanography. Answers have begun to emerge from the existing suite of core program measurements; however, sustained sampling is needed to improve our understanding of contemporary ecosystem behavior and our ability to make informed projections of future changes to this ecosystem. HOT continues to focus on providing answers to some of the questions below:
How sensitive are rates of primary production and organic matter export to short- and long-term climate variability?
What processes regulate nutrient supply to the upper ocean and how sensitive are these processes to climate forcing?
What processes control the magnitude of air-sea carbon exchange and over what time scales do these processes vary?
Is the strength of the NPSG CO2 sink changing in time?
To what extent does advection (including eddies) contribute to the mixed layer salinity budget over annual to decadal time scales and what are the implications for upper ocean biogeochemistry?
How do variations in plankton community structure influence productivity and material export?
What processes trigger the formation and demise of phytoplankton blooms in a persistently stratified ocean ecosystem?
References";
String projects_0_end_date "2014-12";
String projects_0_geolocation "North Pacific Subtropical Gyre; 22 deg 45 min N, 158 deg W";
String projects_0_name "Hawaii Ocean Time-series (HOT): Sustaining ocean ecosystem and climate observations in the North Pacific Subtropical Gyre";
String projects_0_project_nid "2101";
String projects_0_project_website "http://hahana.soest.hawaii.edu/hot/hot_jgofs.html";
String projects_0_start_date "1988-07";
String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
String publisher_type "institution";
String sourceUrl "(local files)";
Float64 Southernmost_Northing 22.75;
String standard_name_vocabulary "CF Standard Name Table v55";
String subsetVariables "longitude,latitude";
String summary "Particle flux measurements from the Hawaii Ocean Time-Series (HOT). Particle flux was measured at a standard reference depth of 150 m using multiple cylindrical particle interceptor traps deployed on a free-floating array for approximately 60 h during each cruise. Sediment trap design and collection methods are described in Winn et al. (1991). Samples were analyzed for particulate C, N, P & Si. Typically six traps are analyzed for PC and PN, three for PP, and another three traps for PSi.";
String title "Sediment trap flux measurements from the Hawaii Ocean Time-Series (HOT) project at station ALOHA.";
String version "1";
Float64 Westernmost_Easting -158.0;
String xml_source "osprey2erddap.update_xml() v1.3";
}
}
Data Access Protocol (DAP) and its
selection constraints.
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.