OCADSAccess DataOceanSODA-ETHZ: A global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification NDP-103

NDP-103 (2020)

NCEI Accession 0220059 Data and Documentation Files

OceanSODA-ETHZ: A global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification (NCEI Accession 0220059)

Luke Gregor1, Nicolas Gruber 1

Prepared by Alex Kozyr2

1Environmental Physics, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 Zürich, Switzerland
2National Centers for Environmental Information, National Oceanic and Atmospheric Administration, 1315 East-West Highway, Silver Spring, MD 20910-3282

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Abstract

This NCEI accession contains full marine carbonate system, calculated from machine learning estimates of total alkalinity (TA) and the partial pressure of carbon dioxide (pCO2). The surface-ocean pCO2 presented here is the ensemble mean of 16 two-step clustering-regression machine learning estimates. The ensemble is a combination of eight clustering instances and two regression methods. For the clustering, we use K-means clustering (21 clusters) repeated with different initiations, resulting in slightly different clusters. Two machine learning regression methods are applied to each of these clustering instances. These machine learning methods are feed-forward neural-network (FFNN), and gradient boosted machine using decision trees (GBDT). The average of the ensemble members is used as the final estimate. Further, the standard deviation of the ensemble members is an analog of the uncertainty. The same two-step clustering-regression approach is used to estimate TA. The final estimate is the mean of 16 ensemble members. Each ensemble member has 12 clusters. Support vector regression (SVR) is used as the regression method. Again, the standard deviation of the ensemble members is an analog of the uncertainty. Total alkalinity and pCO2 are then used to solve for the remaining parameters of the marine carbonate system using the PyCO2SYS software. The temperature and salinity products used in this calculation are provided in the file. Phosphate and silicate from the interpolated World Ocean Atlas (2018) product were used. The total scale for pH was used. The product extends from the start of 1985 to the end of 2018.

Please cite this data set as:

Gregor, Luke; Gruber, Nicolas (2020). OceanSODA-ETHZ: A global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification (NCEI Accession 0220059). Version 1.1. NOAA National Centers for Environmental Information Dataset. [access date]

Method citation:

Gregor, L., Gruber, N. 2020. OceanSODA-ETHZ: A global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification. Submitted to Earth System Science Data. 2020

Variables:

COORDINATES
time	(408)	
	units: days since 1985-01-01
	min 1985-01-01
	max 2018-12-01
	calendar: proleptic_gregorian

lat	(180)	
	standard_name: latitude
	min	-89.5
	max	 89.5
	units	degree_north

lon	(360)	
	standard_name: longitude
	min	-179.5
	max	179.5
	units: degree_east

	
VARIABLES


pCO2 (time, lat, lon) standard_name: surface_partial_pressure_of_carbon_dioxide_in_sea_water units: uatm description: surface ocean partial pressure of carbon dioxide estimated with gradient boosted decision trees (lightGBM) and feed-forward neural network (scikit-learn) predictors: xCO2glob, timeSin, timeCos, chlog_globcolour_filled, sst_ostia, sss_soda, u10_era5, v10_era5, mld_holte testing_years: [1985 1990 1995 2000 2005 2010 2015] validation_years: [1987 1992 1997 2002 2007 2012 2017]
pCO2 units: uatm description: standard deviation of the 16 pCO2 ensemble members as an analog of uncertainty
TAlk (time, lat, lon) units: umol/kg description: Total alkalinity estimated with Support vector regression (scikit-learn) predictors: sst_ostia, sss_soda, nitrateStar_woa18, silicate_woa18, adt_aviso_clim testing_years: [1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016] validation_years: [0]
TAstd (time, lat, lon) units: uatm description: standard deviation of the 16 TA ensemble members as an analog of uncertainty
DIC (time, lat, lon) standard_name: dissolved_inorganic_carbon_in_sea_water short_name: dic units: umol/kg description: DIC calculated with pyCO2SYS
pH (time, lat, lon) standard_name: pH short_name: pH units: -log(H+) description: pH calculated with pyCO2SYS scale: total
HCO3 (time, lat, lon) long_name: Bicarbonate concentration units: umol/kg
omegaAR (time, lat, lon) short_name: omegaAr units: ratio description: aragonite saturation state, where values below are theoretically undersaturated
omegaCA (time, lat, lon) short_name: omegaCa units: ratio description: calcite saturation state, where values below are theoretically undersaturated
salinity (time, lat, lon) standard_name: sea_water_salinity units: psu source: https://www2.atmos.umd.edu/%7Eocean/index_files/soda3.4.2_mn_download_b.htm title: MOM5_SODA_3.4.2 reference: Carton, J.A., G.A. Chepurin, and L. Chen, 2018a: SODA3: a new ocean climate reanalysis, J.Clim., 31, 6967-6983, DOI:10.1175/JCLI-D-18-0149.1. long_name: Practical Salinity
temperature (time, lat, lon) standard_name: sea_surface_foundation_temperature units: degC title: OSTIA Sea Surface Temperature and Sea Ice Analysis long_name: analysed sea surface temperature reference: Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., … Donlon, C. J. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific Data, 6(1), 223. https://doi.org/10.1038/s41597-019-0236-x DSD_entry_id: UKMO-L4HRfnd-GLOB-OSTIARAN source: https://resources.marine.copernicus.eu/?option=com_csw&task=results?option=com_csw&view=details&product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011 sst_type: foundation
Last modified: 2020-09-23T19:15:13Z