This dataset includes over 20,000 depth profiles collected with underwater gliders in a total of 18 missions between 2008 and 2023. The glider missions were centered at the long-term sampling site of the Hawaii Ocean Time-series (HOT) program, Station ALOHA (22°45′N, 158°W), within the North Pacific Subtropical Gyre. The gliders were equipped with sensors to measure temperature, salinity, pressure, dissolved oxygen concentration, chlorophyll a concentration from fluorescence (excitation/emission lambda = 470/695 nm), and the particulate backscattering coefficient at three wavelengths (lambda = 470 nm, 700 nm, and either 650 or 660 nm depending upon mission). Vertical profiles down to at least 200 m were collected for all sensors over periods of several months per mission. Chlorophyll a and oxygen concentrations are calibrated with discrete observations. Particulate backscattering coefficients are corrected with an additional dark subtraction. This dataset differs from the raw data files in that it is quality controlled, calibrated, and corrected. Raw data files can be found at https://hahana.soest.hawaii.edu/seagliders/index.php. The glider observations were collected over the years by Dr. Karl, with different funding sources (including NSF) to investigate carbon cycle processes in the oligotrophic North Pacific Subtropical Gyre. Dr. Ferron and Dr. Barone received NSF funding to conduct a re-analysis of this comprehensive dataset in order to characterize metabolic rates and understand their patterns of variability. Dr. Garcia conducted the data processing that resulted in this curated dataset. Steve Poulos operated the Seagliders in all missions.

Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

This BCO-DMO dataset page contains supporting metadata and CSV formatted datasets for seaglider deployments from 2008 to 2023. Additional NetCDF formatted datasets, containing parameters in the CSV formatted datasets along with raw data and QC values, for seaglider deployments from 2008 to 2023 are available at Zenodo, DOI: 10.5281/zenodo.10416616. See the “Related Datasets” section.

All the seaglider deployments dataset files have the same parameters which are defined on this dataset page. Individual seaglider deployments did not measure all of the parameters, though, e.g. either bbp650 or bbp660, and in that case, values are left blank for unmeasured parameters. 

Parameters in common with the NetCDF formatted datasets and the CSV formatted datasets are: ISO_DateTime_UTC, lat, lon, depth, divenum, direction, temp, cond, salin, sigma, oxygen, chla, bbp470, bbp470_spikeflag, bbp650, bbp650_spikeflag, bbp660, bbp660_spikeflag, bbp700, and bbp700_spikeflag.

Parameters specific to the CSV formatted datasets are: seaglider_deployment, seaglider_platform, and seaglider_mission.

Additional parameters included in the NetCDF files are pressure, pitch, raw data, chla_spikeflag, and QC flags: press, pitch, oxygen_raw,  chla_raw, chla_spikeflag, bbp470_raw, bbp650_raw, bbp660_raw, bbp700_raw, temp_qcflag, cond_qcflag, salin_qcflag, sigma_qcflag, oxygen_qcflag, chla_qcflag, bbp470_qcflag, bbp650_qcflag, bbp660_qcflag, and bbp700_qcflag

The supplemental file seaglider_deployments_metadata.csv contains the locations and dates of each seaglider deployment and recovery via ships and small boats along with the locations and dates of data collection for each seaglider deployment. Additional metadata columns with flags of True or False were included to indicate if the following parameters are measured in an individual deployment dataset: oxygen, bbp650, bbp660, and bbp700. These flag columns were added because deployments either measured bbp650 or bbp660, and oxygen and bbp700 are not always measured during a deployment.  All the seaglider deployments dataset files have the same parameters to enable datasets to be more easily queried and combined programmatically.

Vertically resolved data were collected by underwater Seagliders, which are guided autonomous underwater vehicles developed at the University of Washington’s Applied Physics lab (Eriksen et al., 2001). The Seagliders were piloted either in a circular or 'bowtie' pattern for several months in each mission around Station ALOHA.

Gliders were equipped with the sensors to measure temperature and salinity (Seabird CT Sail), pressure (Paine Electronics 211-75-710-05 and Kistler 4260M00), dissolved oxygen (Aandera models 5013 and 4330), chlorophyll fluorescence and optical backscatter at three wavelengths: 470 nm, 700 nm, and either 650 or 660 nm (WET Labs ECO Triplet ).

The raw observations were quality-controlled, accounting for known sensor responses and deviations from factory calibrations. Detailed descriptions of the data processing, quality control metrics, and calibration methods are provided by Garcia et al. (2023; https://doi.org/10.5281/zenodo.10416616). Quality control steps included a pitch angle test (to detect density inversions), global and regional range tests, stuck value test, biofouling evidence test, and visual inspection. If a test passed, the data were assigned a QC value of 1 (‘good data’). If a test failed (e.g. out of range or density inversion tests), data were flagged, either as ‘questionable data’ (QC = 3) or as ‘bad data’ (QC = 4). All quality control flags and adjustments (i.e. calibration and correction parameters) are preserved in each mission’s file.  Code for all processing steps is available on GitHub: https://github.com/cathygarcia/SeagliderDataprocessing.

Data processing steps for each variable are summarized below:

Temperature, Conductivity, Salinity, and Potential Density Anomaly

• Both temperature and conductivity profiles were lag corrected.
• Practical salinity was calculated using the Gibbs Seawater Toolbox (McDougall et al., 2011), (gsw_SP_from_C.m), and then converted to absolute salinity (gsw_SA_from_SP.m). 
• Potential density anomaly was calculated with respect to a reference water pressure of 0 db using the Gibbs Seawater Toolbox (gsw_sigma0.m).

Dissolved oxygen concentrations

• Raw optode phase values proceeded through a series of corrections to account for the effects of temperature, salinity, pressure, and time response in addition to sensor drift (Bittig et al., 2018, Barone et al., 2019).
• Optode phase values were converted to dissolved oxygen concentrations, and re-calibrated using discrete Winkler measurements. 

Chlorophyll a

• Factory-calibrated chlorophyll a observations were re-calibrated using discrete measurements of either HPLC chlorophyll a (16 missions) or fluorometric chlorophyll a (2 missions).
• Daytime chlorophyll a values are not quench corrected, and may be lower than actual values. It is recommended to use nighttime profiles near the surface. 

Backscattering coefficient due to particles (bbp)

• Factory-calibrated bbp values could have a large offset, that was not expected based on natural variability.
• A mission-specific deep dark correction (1st percentile of bbp at 190-200 m) was subtracted for each bbp dataset. Both the uncorrected and corrected data are available.
• Additionally, a spike flag is included based on published protocol (Briggs et al., 2011).

I. Submitted files were processed with the BCO-DMO dataset processing tool Laminar

Overview

The following Seaglider datasets, one for each deployment, were submitted to BCO-DMO as Excel worksheets with two sheets in each file, a sheet with the dataset and a sheet with dataset metadata:
sg146_4_qc_pass.xlsx, sg146_6_qc_pass.xlsx, sg146_9_qc_pass.xlsx
sg146_5_qc_pass.xlsx, sg146_7_qc_pass.xlsx sg147_4_qc_pass.xlsx
sg148_11_qc_pass.xlsx sg148_16_qc_pass.xlsx sg148_8_qc_pass.xlsx
sg148_12_qc_pass.xlsx sg148_6_qc_pass.xlsx sg148_9_qc_pass.xlsx
sg511_21_qc_pass.xlsx
sg512_1_qc_pass.xlsx sg512_3_qc_pass.xlsx sg512_4_qc_pass.xlsx
sg626_1_qc_pass.xlsx, sg626_4_qc_pass.xlsx

These files were processed and combined in 3 different ways. 1) First the submitted files were processed for each deployment. These processed datasets were saved using the naming format of 928732_suppl_seaglider_obs_sta_aloha_.csv where is the seaglider deployment id. These files are in the “Supplemental Files” section of the BCO-DMO dataset page. 2) Second, the submitted files were processed for each deployment and then deployment datasets in the same year were combined. The deployments for 2010 and 2011 overlapped, so a combined dataset was created using both 2010 and 2011 deployment datasets. These processed datasets were saved using the naming format of 928732_suppl_seaglider_obs_sta_aloha_.csv where is the common year or years of deployment. These files are in the “Data Files” section of the BCO-DMO dataset page. 3) Third, the datasets where deployments were combined by year in Laminar were all combined into one dataset spanning the years 2008 to 2023. The processed combined dataset was saved as 928732_v1_seaglider_observations_sta_aloha_2008_2023.csv. This file is in the “Data Files” section of the BCO-DMO dataset page.

Processing common to each of the 3 datasets processing steps

Using the BCO-DMO laminar data processing tool, each submitted seaglider Excel file with the naming format of _qc_pass.xlsx where is the lower-cased deployment value, e.g. sg146_4 were loaded into Laminar. The datasets spanned from 2008 to 2023. The Excel sheets labeled ‘Data’ were used as the deployment datasets to process.
Columns were added to each deployment dataset for any parameters not recorded in the deployment to have all datasets contain the same parameters. For example, if the parameter bbp650 is missing from a dataset because it was not measured for a deployment, an empty parameter column named bbp650 was added. Possible missing measured parameters include one or more of the following: oxygen, bbp650, bbp660, and bbp700.
Three columns containing the seaglider deployment ID, seaglider platform ID, and seaglider mission number were added to each deployment dataset to differentiate the datasets. The parameter ‘time’ was renamed to the BCO-DMO parameter name ‘ISO_DateTime_UTC’ to better represent the parameter containing UTC datetime values in the ISO8601 format. The latitude and longitude values were rounded from 10 to 8 digit precision which is approximately a resolution of 1.11 mm. The columns were reordered so that all deployment datasets have the same column order.

1) Processing for individual deployments

These individual deployment processed datasets using the common processing steps described earlier were saved using the naming format of 928732_suppl_seaglider_obs_sta_aloha_.csv where is the seaglider deployment id. These files are in the “Supplemental Files” section of the BCO-DMO dataset page.

2) Processing to combined deployments by year

These individual deployment processed datasets using the common processing steps described earlier were combined by deployment year. The deployments for 2010 and 2011 overlapped, so a combined dataset was created using both 2010 and 2011 deployment datasets. These combined processed datasets were saved using the naming format of 928732_suppl_seaglider_obs_sta_aloha_.csv where is the common year or years of deployment. These files are in the “Data Files” section of the BCO-DMO dataset page.

3) Processing to combine all deployments

The yearly combined processed datasets created in step 2 were combined into one dataset. The processed combined dataset was saved as 928732_v1_seaglider_observations_sta_aloha_2008_2023.csv. This file is in the “Data Files” section of the BCO-DMO dataset page.

II. Metadata table created

After the files were processed with Laminar, a metadata table was created using information from the BCO-DMO submission and the deployment datasets. The metadata table was saved as the file seaglider_deployments_metadata.csv in the ‘Supplemental Files’ section on the BCO-DMO dataset page. The definitions of the parameters in the metadata table were saved in the file parameters_for_seaglider_deployments_metadata.csv in the ‘Supplemental Files’ section on the BCO-DMO dataset page.

This metadata table contains the locations and dates of each seaglider deployment and recovery via ships and small boats along with the locations and dates of data collection for each seaglider deployment. Additional metadata columns with flags of True or False were included to indicate if the following parameters are measured in an individual deployment dataset: oxygen, bbp650, bbp660, and bbp700. These flag columns were added because deployments either measured bbp650 or bbp660, and oxygen and bbp700 are not always measured during a deployment.

Coverage: North Pacific Subtropical Gyre

NSF Award Abstract:
Aquatic photosynthesis and respiration rates regulate the flux of organic matter into the ocean’s interior, a process that impacts Earth’s climate by sequestering carbon dioxide from the atmosphere and that provides most of the energy necessary to support the requirements of the organisms inhabiting the dark depths of the ocean. Recent improvements in sensor technology enabled the estimation of photosynthesis and respiration using accurate measurements of the concentration of oxygen dissolved in seawater collected by autonomous underwater vehicles and floats, even in regions of the ocean with low biological activity such as the subtropical gyres.

This project is analyzing data collected in the North Pacific ocean during 7 years using autonomous underwater vehicles in order to obtain an unprecedented number of estimates of metabolic rates for a region of the ocean that is representative of one of the largest oceanic ecosystems. This novel analysis helps constrain the amount of oxygen produced in the sea and improves our understanding of how variations in photosynthesis and respiration influence the flux of organic carbon towards the bottom of the ocean. Two undergraduate students from the University of Hawaii are supported and trained as part of this project. This project is analyzing publicly available observations of temperature, salinity, dissolved oxygen, chlorophyll fluorescence, and optical backscatter collected using underwater gliders in the North Pacific Subtropical Gyre between 2008 and 2014 (>1,000 days of observations). The analyses are used to: (i) quantify in situ rates of gross primary production and respiration in the mixed layer from diel oxygen oscillations, and determine their short-term variability and seasonality; (ii) quantify the net biological oxygen production (both in the mixed layer and in the lower euphotic zone) and determine its seasonality; (iii) quantify annual net community production, from which one can infer the net biological flux of organic C into the ocean’s interior; and (iv) assess how temporal changes in biomass are linked to changes in metabolic rates by comparing oxygen-based metabolic rates with optical proxies of phytoplankton biomass (backscatter and chlorophyll fluorescence). This investigation will better constrain the role of the ocean in regulating Earth’s climate by improved understanding of the mechanisms driving the temporal variability of metabolic rates in the oligotrophic ocean that covers a large fraction of our planet.