How Data Set Characteristics Influence Ocean Carbon Export Models
How Data Set Characteristics Influence Ocean Carbon Export Models
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DOI：
10.1029/2018gb005934
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发表时间：
2018-09-01
影响因子：
5.2
通讯作者：
Buesseler, K. O.
中科院分区：
文献类型：
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作者：
Bisson, K. M.;Siegel, D. A.;Buesseler, K. O.
海洋生物过程每年介导大约10千斤的碳从海洋表面到深海的运输，因此在全球碳循环中起着重要作用。即便如此，全球海洋表层碳输出的综合速率仍然高度不确定。量化这种生物碳输出背后的过程需要综合模型预测和颗粒有机碳（POC）通量的现有观测；然而，模型和观测之间的尺度差异使得这种综合变得困难。在这里，我们比较了由机制模型预测的碳输出量与从文献中收集的跨越不同空间、时间和深度尺度以及使用不同观测方法的几个数据集的POC通量观测值。我们优化了模型参数，以提供模型预测和观测到的POC通量之间的最佳匹配，明确考虑了与每个数据集相关的误差来源。根据用于优化模型的数据集，模式预测的全球散光层底部POC通量综合值在3.8至5.5 Pg C/年之间。模拟的碳输出路径也因用于优化模型的数据集以及用于驱动模型的卫星网初级生产数据产品而异。这些发现强调了收集对碳输出通量的大量自然时空变异性进行平均的实地数据以及推进海洋净初级生产的卫星算法的重要性，以便改进对生物碳输出的预测。
Ocean biological processes mediate the transport of roughly 10 petagrams of carbon from the surface to the deep ocean each year and thus play an important role in the global carbon cycle. Even so, the globally integrated rate of carbon export out of the surface ocean remains highly uncertain. Quantifying the processes underlying this biological carbon export requires a synthesis between model predictions and available observations of particulate organic carbon (POC) flux; yet the scale dissimilarities between models and observations make this synthesis difficult. Here we compare carbon export predictions from a mechanistic model with observations of POC fluxes from several data sets compiled from the literature spanning different space, time, and depth scales as well as using different observational methodologies. We optimize model parameters to provide the best match between model-predicted and observed POC fluxes, explicitly accounting for sources of error associated with each data set. Model-predicted globally integrated values of POC flux at the base of the euphotic layer range from 3.8 to 5.5 Pg C/year, depending on the data set used to optimize the model. Modeled carbon export pathways also vary depending on the data set used to optimize the model, as well as the satellite net primary production data product used to drive the model. These findings highlight the importance of collecting field data that average over the substantial natural temporal and spatial variability in carbon export fluxes, and advancing satellite algorithms for ocean net primary production, in order to improve predictions of biological carbon export.