Quantify disturbance impacts on feedbacks between Arctic permafrost and global climate

Project Information

Q-ARCTIC

Grant agreement ID: 951288

DOI

10.3030/951288

EC signature date 3 March 2021

Start date 1 October 2021

End date 30 September 2027

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 9 979 335,00

EU contribution

€ 9 979 335,00

Coordinated by

MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV
Germany

Periodic Reporting for period 2 - Q-ARCTIC (Quantify disturbance impacts on feedbacks between Arctic permafrost and global climate)

Reporting period: 2023-04-01 to 2024-09-30

Summary of the context and overall objectives of the project

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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Surface-based field work activities within Q-ARCTIC, originally planned to be executed in Siberia, were re-located to sites in Canada and Scandinavia as a consequence of the Russian invasion in the Ukraine in February 2022. First successful campaigns with portable chambers that allow to measure the exchange of greenhouse gases between surface and atmosphere at small scales (<1 m) took place in 2022 and 2023, and the obtained datasets are being processed and analyzed at the time of reporting. Furthermore, a hexacopter-drone, equipped with instruments for measuring greenhouse gases and additional parameters such as e.g. temperature and winds, has been successfully tested under Arctic conditions. Also, a floating chamber for measuring water-atmosphere exchange fluxes has been developed, and successfully applied in a first campaign. Both systems will be applied in future campaigns, and greatly enhance our capabilities to sample greenhouse gas signals at landscape scales. Furthermore, new experimental work was complemented by the collection and formatting of existing pan-Arctic datasets into a comprehensive meta-database on monitoring infrastructure, available online at https://cosima.nceas.ucsb.edu/carbon-flux-sites/.

Research on satellite remote sensing placed a particular focus on the investigation of fine scale (few meters) patterns in Arctic landscapes that are undergoing modifications linked to climate change. To support the development of new technologies, our team gathered spatially-explicit fine scale information from various sources, including new data provided by the surface-based and drone measurement programs within Q-ARCTIC. Topics investigated include for example sinking surfaces, wetness gradients in heterogeneous landscapes, or the characterization of degradational features within thawing permafrost through e.g. land cover succession in drained lake basins. Different trajectories of change could be identified and quantified across bioclimatic zones. At larger scales, we focused on the spatial context of permafrost degradation across the Arctic. This includes the identification of degradational features themselves, for example a semi-automatized retrieval of drained lake basins across the entire Arctic. A novel representation of land cover for the Arctic has been developed that, for the first time, provides a high-resolution map for the entire region, including a representation of different wetlands, lakes and heterogeneity of Arctic landscapes in general.

Using remotely sensed data, we are developing new model components that capture the statistics of small-scale features, e.g. depressions linked to sinking surfaces, or surface water bodies that form when soil ice melts. These promising approaches will facilitate to include the effects of finely-structured landscapes into a coarser-scale model. Representing these processes in the ESM also requires a flexible model infrastructure that allows dealing with the heterogeneity of the surface at various length scales. A suitable scheme for this purpose, a multilayered, interactive tiling scheme, was introduced and tested. Finally, good progress with respect to future projections of permafrost sustainability and the impact of permafrost degradation on global climate has been made. We found that a modification of the permafrost hydrology within the uncertainty range leads to a strong divergence in hydroclimate response. Changes in energy and water cycling due to permafrost thaw have an effect on important cryospheric components of the Earth system. The effects are not limited to the high northern latitudes but also affect a strength of the Atlantic meridional overturning and reach as far south as the tropics, with implications for droughts in the Amazonian forest and vegetation in northern Africa. The speed with which the Arctic warms affects important weather systems and large-scale circulation patterns. These, in turn, determine precipitation rates in the tropics and subtropics and, consequently, the extent of wetlands and the respective methane emissions. Therefore, 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 in permafrost affected regions.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

As already outlined above, both the process model infrastructure and the available databases to inform these models are currently inadequate for representing fine-scale variability in Arctic landscapes, including the important disturbance processes. The Q-ARCTIC consortium addresses these shortcomings from multiple angles, and within several scientific disciplines, in all cases advancing the science beyond the state of the art. We expect that this will lead to a major breakthrough towards forecasting the future state of Arctic permafrost under climate change.

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