Model-data Fusion Approaches for Retrospective and Predictive Assessment of the Pan-Arctic Scale Permafrost Carbon Feedback to Global Climate

Lead Investigator: 
Daniel J. Hayes
Participating Staff: 
Guangsheng Chen
Santonu Goswami
Xiaofeng Xu
Andrew Balser
University of Florida, Boston University, University of Tennessee, University of Alaska – Fairbanks, NASA Jet Propulsion Laboratory
US DOE, Office of Science, Biological and Environmental Research
Start Date: 
July 2012
End Date: 
June 2016

Policy decisions regarding climate change mitigation and adaptation rely on having reliable projections of future climate change. Coupled climate-carbon models predict that the northern high latitudes will serve as a substantial land carbon sink during the 21stcentury, but these models lack many of the key processes governing high-latitude ecosystem processes. Arctic tundra and boreal forests are unique from other biomes particularly because of the presence of snow, ice and frozen ground, and it is important to understand the mechanisms that drive interconnected processes in these landscapes. Increasing our confidence in climate projections requires an integrated approach where existing, on-going and planned observational and experimental studies of high-latitude ecosystem processes are organized and analyzed in a framework designed to target improvements in understanding key ecosystem-climate feedbacks.

We propose an approach to improving our scientific understanding of the permafrost carbon feedback that works from the “top-down” perspective, which will provide a much needed first-order, broad-scale assessment of the drivers and responses of the Arctic system carbon cycle using data-driven models of simple-to-intermediate complexity. The overall goal of the proposed project is to provide the first of its kind, data-driven quantitative assessment of the impact of permafrost thaw on pan-Arctic scale carbon cycle feedbacks to the global climate system. Specifically, the research proposed here is designed to construct an alternative but complimentary conceptual framework for integrating the key system components and their underlying data within a “book-keeping” framework for broad-scale assessment of the key processes driving the permafrost carbon feedback. This approach will be designed to provide quantitative model-data benchmarking to constrain future projections of carbon cycle responses to a warming Arctic. The project will synthesize current and planned data collections related to the key system components from observational and experimental research networks into a pan-Arctic scale framework for identifying and understanding the key drivers, impacts and consequences of permafrost dynamics under a warming Arctic. We will develop new, broad-scale environmental proxy data sets examining recent-era geomorphologic land-form transitions and vegetation dynamics, through remotely-sensed data, to guide the extrapolation of site-based data on the key system components to larger regions. These products will guide the development of finer-scale, more complex representation of permafrost carbon processes in terrestrial biogeochemistry models, to operate within coupled Earth system modeling frameworks.


This work will provide a critical bridge between the abundant but disordered observational and experimental data collections and the development of higher-complexity process representation of the Arctic system carbon cycle in Earth System Modeling frameworks. This proposed study will draw from current and planned observational and experimental data collections and leverage collaboration with scientific networks focused on high-latitude research. In turn, the methods developed here will provide a framework for incorporating and synthesizing the data collections across these various related efforts. Such a framework will provide much needed benchmarking data sets specific to high latitude processes, and this project will demonstrate the use of this information toward model development and evaluation.