Arctic permafrost, perennially frozen ground, is as a critical element of the global climate system. Permafrost areas store an enormous amount of carbon that has been accumulated over timeframes of many thousand years. Rising temperatures within a warmer global climate threaten to thaw parts of these soils and release carbon into atmosphere and water bodies. Thaw-induced emissions of CO2 and CH4 would further accelerate ongoing climate change, leading to even higher temperatures, and further thaw. Since large parts of the Arctic are a fine-scale mosaic of e.g. wetlands, lakes, dry tundra or shrubland, with each of these units showing individual reactions when climate changes, it is very challenging to accurately represent such landscapes in a regional or global-scale model. The task is further complicated by potential disturbance processes: many Arctic ecosystems can abruptly change when permafrost thaws, e.g. lakes can drain when their shores erode, or hills will start sliding when their thawed slopes get unstable. One important factor in this context is the spatial resolution, since Earth System models (ESMs) cannot be operated at grids that are fine enough to represent all important details within Arctic landscapes.
Even though the Arctic region may seem very remote from the European perspective, its state and future development are highly relevant for our well-being. The destabilization of its permafrost carbon pool may lead to future greenhouse gas emissions that rival those of a big industrialized nation. Still, because of the multitude of pathways in which climate change may interact with the complex Arctic ecosystems, we do not yet have the modeling capacities to produce reliable forecasts, and all currently available numbers on future emissions are highly uncertain.
Our project, Q-ARCTIC, will establish a novel land-surface model within an ESM that resolves highest resolution landscape features and disturbance processes in the Arctic. To reach this goal Q-ARCTIC combines expertise in Earth System Modeling, earth observations with satellite-based remote sensing, atmospheric modeling and surface-based observational methods. All components are essential for our objective to generate a reliable, process-based projection of the state of Arctic permafrost under future climate scenarios with a focus on abrupt changes.
