COUP - Constraining uncertainties in the permafrost-climate feedback
Principal Investigator: Assoc. Prof. Christina Biasi, Ph.D. Narasinha Shurpali / Dr. Gustaf Hugelius, Stockholm University, Sweden.
Funding period: 2014-2017
As the global climate warms, thawing permafrost may lead to increased greenhouse gas release from Arctic and Boreal ecosystems. Scientists agree that this permafrost-climate feedback is important to the global climate system, but its magnitude and timing remains poorly understood. The overall aim of COUP is to use detailed understanding of landscape-scale processes to improve global scale climate models. Better predictions of how permafrost areas will respond to a warming climate can help us understand and plan for future global change. In recent years much scientific progress has been made towards understanding the complex responses of permafrost ecosystem to climate warming. Despite this, large challenges remain when it comes to including these processes in global climate models. Permafrost ecosystems are highly variable and studies show that very detailed field investigations are needed to understand complexities. Because global scale models cannot run at such high-resolutions, we propose an approach where local landscape-scale field studies and modelling are used to identify those key variables that should be improved in global models. We will carry out careful field studies and high-resolution modelling at field sites covering all pan-Eurasian environmental
conditions. The system understanding gained from this will then be used to (1) scale key variables so they are useful for global models and (2) improve a new global climate model. In the final step, the improved global climate models will be run to quantify the impact of thawing permafrost on the global climate. Datasets produced in COUP will be freely available online so that they can be used by other scientists and help improvement of all global climate models. COUP is designed to maximize synergies with ongoing projects. Much of the needed data and system understanding was generated in other research programmes.