Q-Arctic: synergetic observations and modeling of pan-Arctic interactions between hydrology, disturbance and carbon cycle processes

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 complex interactions between physical, hydrological, biogeochemical, and ecological processes. Many of these are not well understood to date, and therefore also only rudimentarily represented in current Earth System Models (ESMs).

The Q-ARCTIC project funded by the European Research Council (ERC) follows a synergetic approach by combining remote sensing and local-scale observations with modeling on scales from a few meters to hundreds of kilometers. The primary objective of Q-ARCTIC is to close the gap between process scales and the much coarser grid resolution of Earth System Models (ESMs), with a particular focus on the net effect of disturbance processes and associated changes in hydrology on the pan-Arctic scale. To close this gap, we developed new ESM modules representing subgrid through stochastic parameterizations, trained and evaluated with high-resolution remote sensing data and site-level observations.

We will present novel results from in-situ experiments that quantify carbon fluxes and environmental response functions at patch-level within heterogeneous Arctic ecosystems, resulting in optimized strategies to integrate data streams for upscaling. Satellite remote sensing products investigate fine scale (few meters) patterns in Arctic landscapes that are undergoing modifications linked to climate change, including e.g. InSAR data to constrain ground ice content, and related subsidence patterns. Targets investigated include for example sinking surfaces, wetness gradients in heterogeneous landscapes, and drained lake basins. Assimilation of these new datasets supported the development of new ESM model components that successfully capture the statistics of small-scale features, including e.g. lateral connections between hydrologic landscape elements across scales, or thermokarst lake dynamics. Our results demonstrate that the ability to project the response of the high-latitude water, energy and carbon cycles to rising global temperatures may strongly depend on the ability to adequately represent the soil hydrology and disturbance effects in permafrost affected regions.