Deconvolving the Fate of Carbon in Coastal Sediments

Tessa S. Van der Voort, Utsav Mannu, Thomas M. Blattmann, Rui Bao, Meixun Zhao, Timothy I. Eglinton
2018 Geophysical Research Letters  
Coastal oceans play a crucial role in the global carbon cycle, and are increasingly affected by anthropogenic forcing. Understanding carbon cycling in coastal environments is hindered by convoluted sources and myriad processes that vary over a range of spatial and temporal scales. In this study, we deconvolve the complex mosaic of organic carbon manifested in Chinese Marginal Sea (CMS) sediments using a novel numerical clustering algorithm based on 14 C and total OC content. Results reveal five
more » ... regions that encompass geographically distinct depositional settings. Complementary statistical analyses reveal contrasting region-dependent controls on carbon dynamics and composition. Overall, clustering is shown to be highly effective in demarcating areas of distinct organic facies by disentangling intertwined organic geochemical patterns resulting from superimposed effects of OC provenance, reworking and deposition on a shelf region exhibiting pronounced spatial heterogeneity. This information will aid in constraining region-specific budgets of carbon burial and carbon cycle processes. Plain Language Summary In the context on ongoing climate change, it is crucial to understand how and where carbon is buried. Coastal oceans are very important areas for carbon burial globally, even though they only form a small part of the total ocean surface. These areas are very complex because there is carbon coming both from the land as well as the sea. By understanding where thecarbon from land and where the carbon from the sea ends up, we can better estimate carbon storage. This paper presents a clustering approach which uses the large dataset of carbon age and concentration in the Chinese marginal seas. The clustering approach shows where the carbon from land goes and how it is buried, which areas lose carbon and which areas bury carbon. This approach could also be used in the future on other datasets such as the Arctic Seas.
doi:10.1029/2018gl077009 fatcat:niva2trkcbd5dkzfuui3tt5y6q