Imbalance in the carbonate budget of surficial sediments in the North Atlantic Ocean : millenium time scale variation ?

International audienc

Imbalance in the carbonate budget of surficial sediments in the North Atlantic Ocean: variations over the last millennium?

Fluxes contributing to the particulate carbonate system in deep-sea sediments were investigated at the BENGAL site in the Porcupine Abyssal Plain (Northeast Atlantic). Deposition fluxes were estimated using sediment traps at a nominal depth of 3000 m and amounted to 0.37±0.1 mmol C m−2 d−1. Dissolution of carbonate was determined using flux of total alkalinity from in situ benthic chambers, is 0.4±0.1 mmol C m−2 d−1. Burial of carbonate was calculated from data on the carbonate content of the sediment and sedimentation rates from a model age based on 14C dating on foraminifera (0.66±0.1 mmol C m−2 d−1). Burial plus dissolution was three times larger than particle deposition flux which indicates that steady-state is not achieved in these sediments. Mass balances for other components (BSi, 210Pb), and calculations of the focusing factor using 230Th, show that lateral inputs play only a minor role in this imbalance. Decadal variations of annual particle fluxes are also within the uncertainty of our average. Long-term change in dissolution may contribute to the imbalance, but can not be the main reason because burial alone is greater than the input flux. The observed imbalance is thus the consequence of a large change of carbonate input flux which has occured in the recent past. A box model is used to check the response time of the solid carbonate system in these sediments and the time to reach a new steady-state is in the order of 3 kyr. Thus it is likely that the system has been perturbed recently and that large dissolution and burial rates reflect the previously larger particulate carbonate deposition rates. We estimate that particulate carbonate fluxes have certainly decreased by a factor of at least 3 and that this change has occurred during the last few centuries

VEDLIoT: Very Efficient Deep Learning in IoT

Kaiser M, Griessl R, Kucza N, et al. VEDLIoT: Very Efficient Deep Learning in IoT. In: Institut of Electrical and Electronics Engineers (IEEE), ed. DATE '22: Proceedings of the 2022 Conference & Exhibition on Design, Automation & Test in Europe. Leuven: European Design and Automation Association; 2022: 963-968