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首页> 外文期刊>Environmental and ecological statistics >Investigating the complex relationship between in situ Southern Ocean pCO(2) and its ocean physics and biogeochemical drivers using a nonparametric regression approach
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Investigating the complex relationship between in situ Southern Ocean pCO(2) and its ocean physics and biogeochemical drivers using a nonparametric regression approach

机译：使用非参数回归方法研究原位南大洋pCO（2）及其海洋物理学和生物地球化学驱动因素之间的复杂关系

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The objective in this paper is to investigate the use of a non-parametric approach to model the relationship between oceanic carbon dioxide (pCO(2)) and a range of ocean physics and biogeochemical in situ variables in the Southern Ocean, which influence its in situ variability. The need for this stems from the need to obtain reliable estimates of carbon dioxide concentrations in the Southern Ocean which plays an important role in the global carbon flux cycle. The main challenge involved in this objective is the spatial limitation and seasonal bias of the in situ data. Moreover, studies have also reported that the relationship between pCO(2) and its drivers is complex. As such, in this paper, we use the non-parametric kernel regression approach since it is able to accurately capture the complex relationships between the response and predictor variables. In this analysis we use the in situ data obtained from the SANAE49 return leg journey between Antarctic to Cape Town. To the best of our knowledge, this is the first time this data set has been subjected to such analysis. The model variants were `developed on a training data subset, and the 'goodness' of the models were assessed on an "unseen" test data subset. Results indicate that the nonparametric approach consistently captures the relationship more accurately in terms of mean square error, root mean square error and mean absolute error, over a standard parametric approach (multiple linear regression). These results provide a platform for using the developed nonparametric regression model based on in situ measurements to predict pCO(2) for a larger spatial region in the Southern Ocean based on satellite biogeochemical measurements of predictor variables, given that satellites do not measure pCO(2).
机译：本文的目的是研究使用非参数方法来模拟海洋二氧化碳（pCO（2））与南部海洋中一系列海洋物理和生物地球化学原位变量之间的关系，这些变量会影响其在海洋中的活动。原位变异性。对此的需要源于对南大洋中二氧化碳浓度的可靠估计的需要，这在全球碳通量循环中起着重要作用。该目标涉及的主要挑战是原位数据的空间局限性和季节性偏差。此外，研究还报告说pCO（2）及其驱动程序之间的关系很复杂。因此，在本文中，我们使用非参数核回归方法，因为它能够准确地捕获响应变量和预测变量之间的复杂关系。在此分析中，我们使用从南极至开普敦之间的SANAE49返程旅程获得的原位数据。据我们所知，这是该数据集第一次受到这种分析。模型变量是在训练数据子集上开发的，而模型的“优良性”是在“看不见的”测试数据子集上评估的。结果表明，与标准参数方法（多元线性回归）相比，非参数方法始终能够更准确地捕获均方误差，均方根误差和平均绝对误差之间的关系。这些结果提供了一个平台，该平台可使用基于原位测量的已开发非参数回归模型，基于对预测变量的卫星生物地球化学测量，来预测南洋较大空间区域的pCO（2），前提是卫星无法测量pCO（2）。 ）。

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