Global input datasets for use in constraints on global seafloor biogenic methane production from deterministic and machine learning modeling

This dataset includes 9 grids used as model input for manuscript "Constraints on global seafloor biogenic methane production from deterministic and machine learning modeling". Additionally, there are four grids (heat flow, total organic carbon, porosity, and crust age) for which variable uncertainty was given.

Grids here are available in xyz (longitude in decimal degrees, latitude in decimal degrees, and variable) ascii file format. Each reference is below is the grids native reference. For more information on the creation of these grids please visit the main manuscript.

Below are respective file names and variable name/units:

Dataset 1: Elevation in Meters (+ indicates above sea level, - below sea level)

Tozer, B., Sandwell, D. T., Smith, W. H. F., Olson, C., Beale, J. R., & Wessel, P. (2019). Global bathymetry and topography at 15 arc sec: SRTM15+. Earth and Space Science, 6. https://doi.org/10.1029/ 2019EA000658

Dataset 2: Seawater Density in Kilograms per Cubic Meter

Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; NOAA Atlas NESDIS 72 (pp. 209).

Dataset 3: Seawater Temperature in Degrees Celcius 

Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; NOAA Atlas NESDIS 72 (pp. 209).

Dataset 4: Seawater Salinity in Percent Salinity Units

Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; NOAA Atlas NESDIS 72 (pp. 209).

Dataset 5: Heat Flow in Milliwatts per Square Meter

Global Heat Flow Compilation Group (2013). Component parts of the World Heat Flow Data Collection. PANGAEA, https://doi.org/10.1594/PANGAEA.810104

Hornbach, M. J., Harris, R. N. & Phrampus, B. J. (2020). Heat flow on the U.S. Beaufort Margin, Arctic Ocean: Implications for ocean warming, methane hydrate stability, and regional tectonics. Geochemistry, Geophysics, Geosystems, 21(5). e2020GC008933. https://doi.org/10.1029/2020GC008933

Dataset 6: Sediment Thickness in Meters

Straume, E. O., Gaina, C., Medvedev, S., Hochmuth, K., Gohl, K., Whittaker, J. M., … Hopper, J. R. (2019). GlobSed: updated total sediment thickness in the world’s oceans. Geochemistry, Geophysics, Geosystems, 20(4), 1756–1772.

Dataset 7: Seafloor Porosity in Fraction

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), 2015GL065279. https://doi.org/10.1002/2015GL065279

Dataset 8: Seafloor Total Organic Carbon in Percent Dry Weight

Lee, T.R., Wood, W.T., & Phrampus, B.J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. Global Biogeochemical Cycles. 33, 37–46, doi:10.1029/2018GB005992.

Dataset 9: Crust Age in Million Years

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, Q04006. https://doi.org/10.1029/2007GC001743

Dataset 10: Seafloor Porosity Uncertainty in Fraction

Dataset 11: Seafloor Total Organic Carbon Uncertainty in Percent Dry Weight

Lee, T.R., Wood, W.T., & Phrampus, B.J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. Global Biogeochemical Cycles. 33, 37–46, doi:10.1029/2018GB005992.

Dataset 12: Heat Flow Uncertainty in Milliwatts per Square Meter

Dataset 13: Crust Age Uncertainty in Million Years

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, Q04006. https://doi.org/10.1029/2007GC001743