A simplified, data-constrained approach to estimate the permafrost carbon-climate feedback: The PCN Incubation-Panarctic Thermal (PInc-PanTher) Scaling Approach

• Main Authors: Koven, Charles D., Schuur, Edward. A. G., Schädel, Christina, Bohn, Theodore, Burke, Eleanor, Chen, Guangsheng, Chen, Xiaodong, Ciais, Philippe, Grosse, Guido, Harden, Jennifer, Hayes, Daniel J., Hugelius, Gustaf, Jafarov, Elchin E., Krinner, Gerhard, Kuhry, Peter, Lawrence, David M., MacDougall, Andrew H., Marchenko, Sergei S., McGuire, A. David, Natali, Sue M., Nicolsky, Dmitry J., Olefeldt, David, Peng, Shushi, Romanovsky, Vladimir, Schaefer, Kevin, Strauss, Jens, Treat, Claire C., Turetsky, Merritt
• Format: Conference Object
• Language: unknown
• Published: 2015
• Subjects: permafrost
• Online Access: https://epic.awi.de/id/eprint/39318/; https://agu.confex.com/agu/fm15/meetingapp.cgi/Paper/74303; https://hdl.handle.net/10013/epic.46506

We present an approach to estimate the feedback from large-scale thawing of permafrost soils using a simplified, data-constrained model that combines three elements: soil carbon (C) maps and profiles to identify the distribution and type of C in permafrost soils; incubation experiments to quantify the rates of C lost after thaw; and models of soil thermal dynamics in response to climate warming. We call the approach the Permafrost Carbon Network Incubation–Panarctic Thermal scaling approach (PInc–PanTher). The approach assumes that C stocks do not decompose at all when frozen, but once thawed follow set decomposition trajectories as a function of soil temperature. The trajectories are determined according to a 3-pool decomposition model fitted to incubation data using parameters specific to soil horizon types. We calculate litterfall C inputs required to maintain steady-state C balance for the current climate, and hold those inputs constant. Soil temperatures are taken from the soil thermal modules of ecosystem model simulations forced by a common set of future climate change anomalies under two warming scenarios over the period 2010 to 2100.