1Centre for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan
2Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, China
Received: 06 Sep 2016 – Discussion started: 25 Oct 2016
Abstract. Reconstructing surface ocean CO2 from scarce measurements plays an important role in estimating oceanic CO2 uptake. There are varying degrees of differences among the 14 models included in the Surface Ocean CO2 Mapping (SOCOM) inter-comparison initiative, in which five models used neural networks. This investigation evaluates two neural networks used in SOCOM, self-organizing maps and feedforward neural networks, and introduces a machine learning model called a support vector machine for ocean CO2 mapping. The technique note provides a practical guide to selecting the models.
Revised: 16 Mar 2017 – Accepted: 24 Mar 2017 – Published: 19 Apr 2017
Zeng, J., Matsunaga, T., Saigusa, N., Shirai, T., Nakaoka, S.-I., and Tan, Z.-H.: Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping, Ocean Sci., 13, 303-313, doi:10.5194/os-13-303-2017, 2017.