Dissolved organic matter at the fluvial–marine transition in the Laptev Sea using in situ data and ocean colour remote sensing

• Published in: Biogeosciences
• Main Authors: Juhls, Bennet, Overduin, Pier Paul, Hölemann, Jens, Hieronymi, Martin, Matsuoka, Atsushi, Heim, Birgit, Fischer, Jürgen
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2019
• Subjects: article; Verlagsveröffentlichung; Arctic; Arctic Ocean; Laptev Sea; Climate change; laptev; permafrost
• Online Access: https://doi.org/10.5194/bg-16-2693-2019; https://noa.gwlb.de/receive/cop_mods_00001291; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00001253/bg-16-2693-2019.pdf; https://bg.copernicus.org/articles/16/2693/2019/bg-16-2693-2019.pdf

River water is the main source of dissolved organic carbon (DOC) in the Arctic Ocean. DOC plays an important role in the Arctic carbon cycle, and its export from land to sea is expected to increase as ongoing climate change accelerates permafrost thaw. However, transport pathways and transformation of DOC in the land-to-ocean transition are mostly unknown. We collected DOC and aCDOM(λ) samples from 11 expeditions to river, coastal and offshore waters and present a new DOC–aCDOM(λ) model for the fluvial–marine transition zone in the Laptev Sea. The aCDOM(λ) characteristics revealed that the dissolved organic matter (DOM) in samples of this dataset are primarily of terrigenous origin. Observed changes in aCDOM(443) and its spectral slopes indicate that DOM is modified by microbial and photo-degradation. Ocean colour remote sensing (OCRS) provides the absorption coefficient of coloured dissolved organic matter (aCDOM(λ)sat) at λ=440 or 443 nm, which can be used to estimate DOC concentration at high temporal and spatial resolution over large regions. We tested the statistical performance of five OCRS algorithms and evaluated the plausibility of the spatial distribution of derived aCDOM(λ)sat. The OLCI (Sentinel-3 Ocean and Land Colour Instrument) neural network swarm (ONNS) algorithm showed the best performance compared to in situ aCDOM(440) (r2=0.72). Additionally, we found ONNS-derived aCDOM(440), in contrast to other algorithms, to be partly independent of sediment concentration, making ONNS the most suitable aCDOM(λ)sat algorithm for the Laptev Sea region. The DOC–aCDOM(λ) model was applied to ONNS-derived aCDOM(440), and retrieved DOC concentration maps showed moderate agreement to in situ data (r2=0.53). The in situ and satellite-retrieved data were offset by up to several days, which may partly explain the weak correlation for this dynamic region. Satellite-derived surface water DOC concentration maps from Medium Resolution Imaging Spectrometer (MERIS) satellite data demonstrate rapid removal of DOC within short time periods in coastal waters of the Laptev Sea, which is likely caused by physical mixing and different types of degradation processes. Using samples from all occurring water types leads to a more robust DOC–aCDOM(λ) model for the retrievals of DOC in Arctic shelf and river waters.