Interactive comment on “A comparative assessment of the uncertainties of global surface-ocean CO 2 estimates using a machine learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?” by Luke Gregor et al.
Interactive comment on “A comparative assessment of the uncertainties of global surface-ocean CO 2 estimates using a machine learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?” by Luke Gregor et al.
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发表时间：
2019
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通讯作者：
P. Landschützer
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作者：
P. Landschützer
(1)提出了一种新的算法，将离散的PCO2测量数据在时空上内插到连续的PCO2场中；(2)从几个度量标准出发，给出并讨论了该算法与现有的PCO2内插方法的比较。关于(1)，虽然该算法的基础是相同的原理(即二氧化碳与更完整测量的驱动量的非线性回归)，但它的不同之处在于，它正式选择了如何将海洋划分为具有相似生物地球化学行为的区域。特别是，选择不是孤立地完成的，而是涉及回归
The authors present (1) a new algorithm to spatiotemporally interpolate discrete pCO2 measurements into continuous pCO2 field, and (2) present and discuss a comparison between this and existing pCO2 interpolations in the light of several metrics. Concerning (1), though the algorithm is based on the same principles (namely non-linear regression of pCO2 against driving quantities measured more completely) which have also been employed by several existing algorithms for the same purpose, it differs by a formalized selection of how to split the ocean into areas of similar biogeochemical behaviour. In particular, the selection is not done in isolation but involves the regression