Thermokarst lakes are common and dynamic features in lowland areas with ice-rich permafrost, playing a crucial role in the exchange of carbon, water, and energy between atmosphere and land surface. They are vital to Arctic lowland hydrology, yet they are often not well-represented in Earth system models (ESMs). Most modeling efforts have focused on individual lakes, because thermokarst lake dynamics depend on small-scale surface and subsurface variations that are hard to measure. This makes deterministic modelling challenging. We instead use a probabilistic modeling approach, incorporating randomness to reflect the diverse behaviors of individual lakes due to varying environmental conditions. We present idealized simulations and test new high-resolution remote sensing data that track annual lake areas, which help in initializing and calibrating our model. While our model marks a step forward in representing permafrost landscapes in ESMs, our research also highlights the need for ongoing remote sensing data collection and additional data on past lake behaviors to improve model accuracy. 

In this study, we improved the ORCHIDEE LSM by using detailed global soil data from a high-resolution database called SoilGrids. This allowed us to better prescribe soil organic and mineral content and to refine the modelling of heat transfer and soil temperatures. We tested this updated model at various instrumented Arctic and boreal sites and compared it to earlier versions. Results showed that including detailed organic matter data improved soil temperature predictions, especially in deeper layers and during snow-free periods, increasing accuracy by up to 25% in some cases. This underscores the value of precise soil data for better climate predictions, particularly in Arctic and boreal regions where organic-rich soils play a key role in the ecosystem.

The Boundary Layer Dispersion and Footprint Model (BLDFM) is a new, accurate tool that tracks how greenhouse gases and pollutants spread in the air. It uses advanced numerical methods without relying on 
simplifying assumptions like traditional models. The model is flexible and works well with different turbulence data or real wind measurements. Tests show it matches exact specific solutions and improves on older models by including more detailed mixing processes. BLDFM is useful for climate studies, pollution monitoring, and understanding emission sources.

The Arctic is warming several times faster than the rest of the globe. Such Arctic amplification rapidly changes hydrometeorological conditions with consequences for the structuring of cold-adapted terrestrial and aquatic ecosystems. Arctic ecosystems are particularly susceptible to hydrometeorological regime shifts thus frequently undergo system-scale transitions. Abrupt ecosystem changes are often triggered by disturbances and extreme events that shift the ecosystem state beyond its buffering threshold capacity thus irreversibly changing its functioning (ecosystem tipping). The tipping depends on spatial and temporal scales. Due to the centennial timescale of soil carbon and vegetation dynamics, Arctic ecosystems are not in equilibrium with the changing climate, so a tipping could occur at a later time. Earth Observation (EO) is useful for monitoring ongoing changes in permafrost and freshwater systems, in particular extreme events and disturbances, as indicators of a possible tipping point.

Lakes are common features in Arctic permafrost areas. Land cover change following drainage needs to be monitored due to its ecological implications and effects on the carbon cycle, particularly regarding transitions between wet and dry conditions. Satellite data are key in this context. We compared a common vegetation index approach with a novel land-cover-monitoring scheme. Land cover information provides specific information on wetland features. We also showed that the bioclimatic gradients play a significant role after drainage within the first 10 years.

The study explores how thawing permafrost could change the Earth's surface structure in the Arctic region. We used advanced simulations (Large Eddy Simulations) to study how different types of surfaces, like grassland and lakes, affect the atmosphere above. It was found that the proportion of lakes in the area has a big impact on the heat exchange with the atmosphere, suggesting a potential feedback mechanism: as the Arctic dries due to climate change, it might speed up permafrost thaw. Additionally, the study highlights that the arrangement of surface features matters, which is important for Earth System Models predicting permafrost changes in the Arctic.

The Russian Federation contains two-thirds of the northern permafrost area. The loss of access to permafrost carbon flux sites and data within Russia, linked to the Russian invasion of Ukraine, threatens the ability of the international science community to detect and quantify the current state of the permafrost-climate feedback. This disruption of science collaboration undermines the effectiveness of the Arctic carbon monitoring network as a tool for monitoring climate change in quantifiable ways. This study found that the representativeness of the pan-Arctic network of eddy covariance flux towers is reduced to about 66% of its full impact when removing the 27 sites located in Russian territory. This loss can only partly be compensated for when installing new sites in Arctic regions outside of Russia. Expanding e.g. North American infrastructure could be shown to be a way forward for climate science, and also satellite remote sensing observations may partly fill the gap, but since there are permafrost ecosystems in Russia that do not have environmental analogs elsewhere, there does not appear to exist an equivalent solution to Russian science collaboration.