The enormous quantities of frozen carbon in the Arctic, presently held in surface soils on land and in shallow subsea sediments, may act as “capacitors” of the global carbon system. Thawing permafrost (PF) and collapsing methane hydrates are top candidates to cause a net transfer of carbon from land and ocean systems to the atmosphere this century, yet uncertainties abound. The CC-Top program targets the East Siberian Arctic Ocean (ESAO), the World’s largest yet shallowest shelf sea, as it holds 80% of coastal PF, 80% of subsea PF and 75% of shallow hydrates. Main objective is to transform the, at present, largely descriptive and data-lean pictures into quantitative understanding of the carbon-climate system, to pin down the present system functioning and thereby enable meaningful predictions of future releases from these “Sleeping Giants” of the global carbon system.

The CC-Top program combines unique Arctic field capacities with powerful molecular-isotopic characterization of PF carbon/methane to make breakthroughs on:

  • The “awakening” of terrestrial PF carbon pools: CC-Top employs the great pan-arctic rivers as natural integrators of processes in their drainage basins and by probing the δ13C/Δ14C and molecular fingerprints, apportions release fluxes of different PF carbon pools.
  • The ESAO subsea cryosphere/methane: CC-Top uses recent spatially-extensive observations, deep sediment cores and gap-filling expeditions to (i) estimate the distribution of subsea PF and hydrates; (ii) establish the thermal state (thawing rate) of subsea PF carbon; (iii) apportion the sources of methane now being released from the sediments between subsea PF, shallow hydrates vs seepage from the deep petroleum megapool, using source-diagnostic triple-isotope fingerprinting.
  • Arctic Ocean slope hydrates: CC-Top investigates sites (discovered by us on expeditions in 2008-2014) of collapsed hydrates actively venting methane, to characterize their geospatial distribution and properties that cause the destabilization.

Project Info

Project start: 2016

Funded by

European Research Council Advanced Grant (grant number 695331) to Örjan Gustafsson