Data Access Form

Dataset Title:	Deep convection simulation from the MITgcm (MIT General Circulation Model) (IVOMLS project)
Institution:	BCO-DMO (Dataset ID: bcodmo_dataset_706167)
Information:	Summary \| License \| ISO 19115 \| Metadata \| Background \| Files

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Variable	Minimum	Maximum
experiment_name (unitless)	"XY_O2_z41_step_120..."	"pro_in_T_z41_step_..."
experiment_type (unitless)	"Dissolved oxygen p..."	"Velocity transect"
comment (unitless)	"cooling duration: ..."	"validation run usi..."
filesize (unitless)	"105K"	"81K"
file_download_link (unitless)	"

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  experiment_name {
    String bcodmo_name "exp_id";
    String description "Experiment name for model output";
    String long_name "Experiment Name";
    String units "unitless";
  }
  experiment_type {
    String bcodmo_name "exp_type";
    String description "Experiment type";
    String long_name "Experiment Type";
    String units "unitless";
  }
  comment {
    String bcodmo_name "comment";
    String description "Experiment description";
    String long_name "Comment";
    String units "unitless";
  }
  filesize {
    String bcodmo_name "file_size";
    String description "File size of the .mat file";
    String long_name "Filesize";
    String units "unitless";
  }
  file_download_link {
    String bcodmo_name "file_link";
    String description "Link to download data as .mat file (HTML link)";
    String long_name "File Download Link";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"All experiments were performed\\u00a0using the MIT General Circulation Model
(MITgcm) [Marshall et al., 1997a, b]. The model was configured to allow non-
hydrostatic dynamics to explicitly resolve deep convection, and the set-up was
modified from Jones and Marshall [1993]. The model domain was a box with
periodic boundary conditions in the x\\u00a0and y\\u00a0directions of 32 x 32 km
with a horizontal\\u00a0resolution of 250 m. The box had a uniform depth of 2
km with 41 z-levels with increasing thickness\\u00a0from 10 m
at\\u00a0surface\\u00a0to 100 m near the bottom. The linear equation of state
was\\u00a0used throughout this study. 16 sensitivity experiments were designed
to explore the behavior of oxygen uptake during the deep convection events
under different cooling conditions. Two validation runs were also applied by
forcing the model using observational data from Argo
[(http://www.argo.net/](\\\\\"http://www.argo.net/\\\\\")). Detailed information
about all simulations can be found in Sun et al [2017]. In this data set,
horizontally averaged profiles and vertical transport of dissolved oxygen and
temperature from all experiments are included. A few transect of dissolved
oxygen and temperature are also included to demonstrate the evolution of the
convection event.\\u00a0  
 \\u00a0  
\\u00a0References:\\u00a0
 
Jones, H., and J. Marshall (1993), Convection with rotation in a neutral
ocean: A study of open-ocean deep convection, Journal of Physical
Oceanography, 23(6), 1009\\u20131039.
 
Marshall, J., C. Hill, L. Perelman, and A. Adcroft (1997a), Hydrostatic,
quasi-hydrostatic, and\\u00a0non-hydrostatic\\u00a0ocean modeling, Journal of
Geophysical Research: Oceans, 102(C3), 5733\\u20135752.
 
Marshall, J., A. Adcroft, C. Hill, L. Perelman, and C. Heisey (1997b), A
finite-volume, incompressible\\u00a0navier\\u00a0stokes\\u00a0model for studies
of the ocean on parallel computers, Journal of Geophysical Research: Oceans,
102(C3), 5753\\u20135766.
 
Sun, D., T. Ito and B. Annalisa, Oxygen flux and vertical transport during
deep convection events,\\u00a0submitted\\u00a0to Global Biogeochemical Cycles.";
    String awards_0_award_nid "706159";
    String awards_0_award_number "OCE-1357373";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1357373";
    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 "Henrietta N Edmonds";
    String awards_0_program_manager_nid "51517";
    String cdm_data_type "Other";
    String comment 
"Deep convection simulation using MITgcm 
  PIs: Takamitsu Ito and Anna Bracco 
     data version 28 Jun 2017 
        All 44 MATLAB .mat files listed below with links to download individual files. 
        Or download a ZIP file containing all .mat files: <a href='http://dmoserv3.whoi.edu/data_docs/IVOMLS/MITgcm/model_output.zip'>model_output.zip (50 MB)</a>";
    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 "2017-06-27T16:16:32Z";
    String date_modified "2017-08-08T11:37:36Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.712322";
    String history 
"2024-04-26T03:40:33Z (local files)
2024-04-26T03:40:33Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_706167.html";
    String infoUrl "https://www.bco-dmo.org/dataset/706167";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, comment, data, dataset, dmo, download, erddap, experiment, experiment_name, experiment_type, file, file_download_link, filesize, link, management, name, oceanography, office, preliminary, type";
    String license "https://www.bco-dmo.org/dataset/706167/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/706167";
    String param_mapping "{'706167': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/706167/parameters";
    String people_0_affiliation "Georgia Institute of Technology";
    String people_0_affiliation_acronym "Georgia Tech";
    String people_0_person_name "Takamitsu Ito";
    String people_0_person_nid "51088";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Georgia Institute of Technology";
    String people_1_affiliation_acronym "Georgia Tech";
    String people_1_person_name "Dr Annalisa Bracco";
    String people_1_person_nid "706163";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Georgia Institute of Technology";
    String people_2_affiliation_acronym "Georgia Tech";
    String people_2_person_name "Daoxun Sun";
    String people_2_person_nid "706173";
    String people_2_role "Contact";
    String people_2_role_type "related";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Amber York";
    String people_3_person_nid "643627";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "IVOMLS";
    String projects_0_acronym "IVOMLS";
    String projects_0_description 
"NSF abstract:
Recent observations and climate model projections indicate that the global oxygen inventory may be declining due to the lower solubility and the increasing stratification associated with a warming climate. Decomposition of organic matter in the deep sea consumes dissolved oxygen, which must be replenished by the circulation of oxygen-rich waters from the polar regions. Without vigorous oxygen supply from the high latitudes, the global oceans will lose oxygen.
In this study, researchers at Georgia Tech will use a hierarchy of models to simulate oxygen and nutrient cycling in the Labrador Sea, one of the regions of deep water formation in the North Atlantic, over a fifty year period. The Labrador Sea is also a region of extreme seasonality and intense biological productivity, thus oxygen cycling there likely reflects multiple physical and biological processes. Results from this study will promote a better understanding of the interannual variability of oxygen and nutrients in the Labrador Sea, and ultimately contribute to knowledge on how a changing climate impacts these cycles.
Broader Impacts:
The broader impacts of this project include student training, international collaboration and outreach to K-12 students.";
    String projects_0_end_date "2018-04";
    String projects_0_geolocation "Labrador Sea";
    String projects_0_name "Interannual variability of oxygen and macro-nutrients in the Labrador Sea";
    String projects_0_project_nid "706160";
    String projects_0_start_date "2014-05";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary 
"This dataset includes deep convection simulation output from the MITgcm (MIT
General Circulation Model). The data include profiles under different
situations in an idealized domain. \\u00a0The experiment was based
on\\u00a0winter Labrador Sea\\u00a0conditions.
 
Model output\\u00a0is\\u00a0available to download as MATLAB .mat files by
clicking the \\Get Data\\ link. \\u00a0Each file includes the output of one run
covering 90 days.  
 * Contents of .mat files described in
\\u00a0[mat_file_variables.csv](\\\\http://dmoserv3.whoi.edu/data_docs/IVOMLS/MITgcm/mat_file_variables.csv\\\\)";
    String title "Deep convection simulation from the MITgcm (MIT General Circulation Model) (IVOMLS project)";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

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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.

(easy) You can get data by using the dataset's Data Access Form or Subset form. They make the URL for you.
(easy) You can make a graph or map by using the dataset's Make A Graph form. It makes the URL for you.
(not hard) You can bypass the forms and get the data or make a graph or map by generating the URL by hand or with a computer program or script.

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.