A global monthly climatology of total alkalinity (AT): a neural network approach (NCEI Accession 0222470)
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https://www.ncei.noaa.gov/archive/accession/0222470
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资源简介：
This dataset contains global monthly climatology of oceanic total alkalinity (AT). Total alkalinity (AT) monthly climatology was created from a neural network approach (Broullón et al., 2019). The neural network was trained with GLODAPv2.2019 data (Olsen et al., 2019) using as predictor variables position (latitude, longitude and depth), temperature, salinity, phosphate, nitrate, silicate and dissolved oxygen. The relations extracted between these predictor variables and AT were used to obtain the climatology passing through the network global monthly climatologies of the predictor variables: temperature and salinity fields of the World Ocean Atlas version 2013 (WOA13), filtered WOA13 oxygen (fifth-order one-dimensional median filter through the depth dimension; see Broullón et al., 2019 for details) and nutrients computed using CANYON-B (Bittig et al., 2018) over the three previous fields. The obtained climatology has a 1ºx1º spatial resolution and 102 depth levels between 0 and 5500 m, with a monthly resolution from 0 to 1500 m and an annual resolution from 1550 to 5500m.
提供机构：
Lamont-Doherty Earth Observatory; University of Groningen; Institute of Marine Research Vigo; Alfred-Wegener Institute, Helmholtz Centre for Polar and Marine Research; Princeton University, Department of Geosciences, Program in Atmospheric and Ocean Sciences; Instituto de Oceanografía y Cambio Global, IOCAG; NOAA National Centers for Environmental Information; GEOMAR Helmholtz Centre for Ocean Research Kiel; Spanish National Research Council; Bjerknes Centre for Climate Research
创建时间：
2020-01-01
