The continental shelves of the Arctic Ocean and surrounding seas contain large stocks of organic matter (OM) and methane (CH4), representing a potential ecosystem feedback to climate change not included in international climate agreements. We performed a structured expert assessment with 25 permafrost researchers to combine quantitative estimates of the stocks and sensitivity of organic carbon in the subsea permafrost domain (i.e. unglaciated portions of the continental shelves exposed during the last glacial period). Experts estimated that the subsea permafrost domain contains ∼560 gigatons carbon (GtC; 170–740, 90% confidence interval) in OM and 45 GtC (10–110) in CH4. Current fluxes of CH4 and carbon dioxide (CO2) to the water column were estimated at 18 (2–34) and 38 (13–110) megatons C yr−1, respectively. Under Representative Concentration Pathway (RCP) RCP8.5, the subsea permafrost domain could release 43 Gt CO2-equivalent (CO2e) by 2100 (14–110) and 190 Gt CO2e by 2300 (45–590), with ∼30% fewer emissions under RCP2.6. The range of uncertainty demonstrates a serious knowledge gap but provides initial estimates of the magnitude and timing of the subsea permafrost climate feedback.

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Area is an integral part of any spatial database and has a significant role in many geographic analyses and applications. Planar algorithms that are widely used to calculate area ignore the slope and curvature of the terrain and result in under-estimation, particularly as pixel size increases or in uneven terrain. Calculating surface area using a regular DEM can overcome this issue by considering localized variations on the terrain surface. This paper investigates the scale- and algorithm-dependence of surface area calculations. The expectation is that for any individual pixel, the improvement in measurements can be relatively small, however, the additive effects across the study area can become significant. The method of dividing each DEM pixel into eight 3D triangles is commonly used to calculate surface area. In this research, the elevation of triangle vertices are estimated using different interpolation methods to establish rates of under-estimation for progressively larger pixels. These methods are validated against vertex elevations on a 3 meter lidar data benchmark. Bi-Cubic interpolation outperforms other interpolation methods for calculating DEM surface areas, with Linear, Bi-Linear and Jenness methods performing nearly as well, especially at coarser resolution. Relative accuracies are shown to degrade somewhat in rougher terrain.

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