Title: Global derived datasets for use in k-NN machine learning prediction of global seafloor total organic carbon

Type Dataset Lee, Taylor R., Wood, Warren T., Phrampus, Benjamin J. (2018): Global derived datasets for use in k-NN machine learning prediction of global seafloor total organic carbon. Zenodo. Dataset. https://zenodo.org/record/1471639

Authors: Lee, Taylor R. (U.S. Naval Research Laboratory) ; Wood, Warren T. (U.S. Naval Research Laboratory) ; Phrampus, Benjamin J. (ASEE Postdoctoral Program, U.S. Naval Research Laboratory) ;

Links

• Item record in Zenodo
• Digital object URL

Summary

This dataset includes 663 predictor grids used for k-NN global prediction of seafloor total organic carbon.

663 predictor grids available in netCDF4 HDF5 file format. Grids are cell-centered sized 4320 x 2160. File names adhere to the naming conventions discussed below. The naming structure is partioned by underscores and periods in the following order: interface to which the gridded values refer to, quantity of values contained within the grid, units and reference values/units (e.g. meters below sea level), data source, statistic calculated (if applicable), grid pitch, and file extension.

Possible interfaces from the top – down:

SS – Sea surface – atmosphere interface (may also be average of the entire water column)

SF – Seafloor – water interface (may also be denoted by GL)

GL   – Ground level (e.g. bottom of pure liquid, top of dirt)

SC – Sediment – crust interface (e.g. sediment above, igneous/metamorphic below)

CM – Crust – mantle interface (e.g. Mohorovicic discontinuity)

Appropriate reference naming marker (bold), original data source, and date of last access:

Becker

Becker, J. J., Wood, W. T., & Martin, K. M. (2014). Global crustal heat flow using random decision forest prediction, Abstract NG31A-3788 presented at 2014 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 06/23/2015.

CRUST1 

Pasyanos, M.E., Masters, G., Laske, G. & Ma, Z. (2012). LITHO1.0 - An Updated Crust and Lithospheric Model of the Earth Developed Using Multiple Data Constraints, Abstract T11D-09 presented at 2012 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 07/01/2014.

CRUST1_NOAA

 As the NOAA sediment thickness database is globally not complete, data gaps in the NOAA grid with this have been supplemented by the CRUST1 sediment thickness (see above citation).

Whittaker, J., Goncharov, A., Williams, S., Müller, R. D., & Leitchenkov, G. (2013) Global sediment thickness dataset updated for the Australian-Antarctic Southern Ocean, Geochemistry, Geophysics, Geosystems. https://doi.org/10.1002/ggge.2018. Last access:  09/02/2018.

GVP

Global Volcanism Program (2013) Volcanoes of the World. In E. Venzke (ed.). (Vol. 4.7.3).  Smithsonian Institution. https://doi.org/10.5479/si.GVP.VOTW4-2013. Last access: 09/22/2014.

ETOPO2v2

National Geophysical Data Center (2006). 2-minute Gridded Global Relief Data (ETOPO2) v2. National Geophysical Data Center, NOAA. DOI: 10.7289/V5J1012Q. Last access: 02/06/2013.

PLATES

Coffin, M.F., Gahagan, L.M., & Lawver, L.A. (1998). Present-day Plate Boundary Digital Data Compilation. University of Texas Institute for Geophysics Technical Report (No. 174, pp. 5). Last access: 09/15/2014.

ONRL

Ludwig,W., Amiotte-Suchet, P., & Probst, J. L. (2011). ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans. In F. G. Hall, G. Collatz, B. Meeson, S. Los, E. Brown de Colstoun, and D. Landis (Eds.), ISLSCP Initiative II Collection. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1028. Last Access: 02/15/2015.

Muller

Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust, Geochemistry, Geophysics, Geosystems, 9(4), Q04006. https://doi.org/10.1029/2007GC001743. Last accessed: 07/19/2011.

Woa13x

Boyer, T.P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., et al. (2013) World Ocean Database 2013. In  S. Levitus, A. Mishonov (Ed.), NOAA Atlas NESDIS 72, Technical Ed. Silver Spring, MD. http://doi.org/10.7289/V5NZ85MT. Last Access: 09/18/2014.

KIM

Kim, S.S. & Wessel, P. (2011). New global seamount census from the altimetry-derived gravity data, Geophysical Journal International, 186, 615-631. https://doi.org/10.1111/j.1365-246X.2011.05076.x.  Last access: 09/22/2014.

HYCOM

The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data.Last access: 03/19/2014.

NCEDC

NCEDC (2016). Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Last access: 09/21/2014.

Wei2010

Wei, C.-L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley, M. J., et al.(2010). Global patterns and predictions of seafloor biomass using random forests. PLoS ONE,5(12), e15323. https://doi.org/10.1371/journal.pone.0015323 Last access: 06/20/2016.

NGA_egm2008

Pavlis, N.K., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2008). The EGM2008 Global Gravitational Model, Abstract 2008AGUFM.G22A..01P presented at the 2008 General Assembly of the European Geosciences Union, Vienna, Austria. Last access: 07/10/2014.

WAVEWATCH3

The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data. Last access: 03/19/2014.

Updated global seafloor porosity grid using our k-nearest neighbors algorithm using 5 nearest neighbors. Observed data used for prediction from Martin et al. (2015). 

Martin, K. M., Wood, W. T., & Becker, J. J. (2015). A global prediction of seafloor sediment porosity using machine learning. Geophysical Research Letters, 42(24), 10640. https://doi.org/10.1002/2015GL065279

Other grids which have been generated by empirical means are latitude (and derivatives), longitude (and derivatives), Coriolis, coast_is_1.0, and the random noise grids. 

Units referenced are as follows:

KGM3 - kilogram per cubic meter MS - meters per second KM - kilometer M_ASL - meters above sea level (i.e. meters referenced to sea level) MWM2 - milliwatt per square meter TGCYR - terragram of carbon per year TGYR - terragram per year MA - megaannum M - meters MGCM2 - milligram of carbon per square meter DEG - degree S - seconds

Statistics grids are calculated within a given radius (e.g. 10km, 50km, 125km, 250km, 500km, 1000km) of the respective cell-centered value. The statistics grids include mean (.men), average absolute deviation from the mean (.aad), and the common logarithm (.log) of the absolute value of the mean (.mlg). Additionally, some grids are a weighted count for given radii (e.g. seamounts) where weight is a cosine taper from the center of the grid cell. 

The grid pitch for this dataset is uniformly at 5-arc minute denoted by “.5m”. Additionally, the extension used (netCDF4) is denoted by “.nc”.

More information

• DOI: 10.5281/zenodo.1471639
• Language: en

Subjects

• globaldatasets, machinelearning, globalprediction

Dates

• Publication date: 2018
• Issued: October 25, 2018

Rights

• https://creativecommons.org/licenses/by/4.0/legalcode Creative Commons Attribution 4.0 International
• info:eu-repo/semantics/openAccess Open Access

Format

electronic resource

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