Arctic permafrost has been identified as a critical element in the global climate system, since it stores a vast amount of carbon that is at high risk of being released under climate change. The feedbacks between permafrost carbon and climate change are moderated by many factors, including hydrology, topography, and biology. Shifts in these factors lead to highly complex feedbacks between biogeochemical and biogeophysical processes. These are only rudimentarily represented in current Earth System Models (ESMs), in particular due to a scaling gap between processes and model grid.
Q-ARCTIC will establish a next generation coupled land-surface model that explicitly resolves highest resolution landscape features and disturbance processes in the Arctic. Model development will be informed by novel remote sensing methodologies linking landscape characteristics and change potential at an exceptional level of detail. Interdisciplinary observations at multiple spatiotemporal scales will deliver novel insight into permafrost carbon cycle processes. All components are essential for our objective to generate an unprecedented process-based hindcast of glacial permafrost carbon state and projection of permafrost sustainability under future scenarios with a focus on abrupt changes.
Our ground-breaking research is based on the newly developed ICON-ESM that enables highest-resolution simulations based on high-performance computing infrastructure. The required remote sensing information can for the first time be produced from new pan-Arctic data streams, such as the European Sentinel satellites. Finally, recent breakthroughs in ultraportable instrumentation and mobile air- and water-borne platforms facilitate bridging the gap between in-situ process understanding and landscape-scale surface-atmosphere exchange. The Q-ARCTIC PI-consortium will combine their world-leading expertise in these fields to close the scaling gap between high-resolution processes and the coarser ESM resolution.
Fields of science
Call for proposal
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Funding SchemeERC-SyG - Synergy grant