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ABSTRACT Research in geocryology is currently principally concerned with the effects of climate change on permafrost terrain. The motivations for most of the research are (1) quantification of the anticipated net emissions of CO₂and CH₄from warming and thaw of near‐surface permafrost and (2) mitigation of effects on infrastructure of such warming and thaw. Some of the effects, such as increases in ground temperature or active‐layer thickness, have been observed for several decades. Landforms that are sensitive to creep deformation are moving more quickly as a result, andRock Glacier Velocityis now part of the Essential Climate VariablePermafrostof the Global Climate Observing System. Other effects, for example, the occurrence of physical disturbances associated with thawing permafrost, particularly the development of thaw slumps, have noticeably increased since 2010. Still, others, such as erosion of sedimentary permafrost coasts, have accelerated. Geochemical effects in groundwater from trace elements, including contaminants, and those that issue from the release of sediment particles during mass wasting have become evident since 2020. Net release of CO₂and CH₄from thawing permafrost is anticipated within two decades and, worldwide, may reach emissions that are equivalent to a large industrial economy. The most immediate local concerns are for waste disposal pits that were constructed on the premise that permafrost would be an effective and permanent containment medium. This assumption is no longer valid at many contaminated sites. The role of ground ice in conditioning responses to changes in the thermal or hydrological regimes of permafrost has re‐emphasized the importance of regional conditions, particularly landscape history, when applying research results to practical problems. 

Remote sensing-based Earth Observation plays an important role in assessing environmental changes throughout our planet. As an image-heavy domain, the evaluation of the data strongly focuses on statistical and pixel-based spatial analysis methods. However, considering the complexity of our Earth system, there are some environmental structures and dependencies that are not possible to accurately describe with these traditional image analysis approaches. One example for such a limitation is the representation of (spatial) networks and their characteristics. In this study, we thus propose a computer vision approach that enables the representation of semantic information gained from images as graphs. As an example, we investigate digital terrain models of Arctic permafrost landscapes with its very characteristic polygonal patterned ground. These regular patterns, which are clearly visible in high-resolution image and elevation data, are formed by subsurface ice bodies that are very vulnerable to rising temperatures in a warming Arctic. Observing these networks’ topologies and metrics in space and time with graph analysis thus allows insights into the landscape’s complex geomorphology, hydrology, and ecology and therefore helps to quantify how they interact with climate change. We show that results extracted with this analytical and highly automated approach are in line with those gathered from other manual studies or from manual validation. Thus, with this approach, we introduce a method that, for the first time, enables upscaling of such terrain and network analysis to potentially pan-Arctic scales where collecting in-situ field data is strongly limited. 

In response to increasing Arctic temperatures, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowlands, polygonal ice wedges are especially prone to degradation. Melting of ice wedges results in deepening troughs and the transition from low-centered to high-centered ice-wedge polygons. This process has important implications for surface hydrology, as the connectivity of such troughs determines the rate of drainage for these lowland landscapes. In this study, we present a comprehensive, modular, and highly automated workflow to extract, to represent, and to analyze remotely sensed ice-wedge polygonal trough networks as a graph (i.e., network structure). With computer vision methods, we efficiently extract the trough locations as well as their geomorphometric information on trough depth and width from high-resolution digital elevation models and link these data within the graph. Further, we present and discuss the benefits of graph analysis algorithms for characterizing the erosional development of such thaw-affected landscapes. Based on our graph analysis, we show how thaw subsidence has progressed between 2009 and 2019 following burning at the Anaktuvuk River fire scar in northern Alaska, USA. We observed a considerable increase in the number of discernible troughs within the study area, while simultaneously the number of disconnected networks decreased from 54 small networks in 2009 to only six considerably larger disconnected networks in 2019. On average, the width of the troughs has increased by 13.86%, while the average depth has slightly decreased by 10.31%. Overall, our new automated approach allows for monitoring ice-wedge dynamics in unprecedented spatial detail, while simultaneously reducing the data to quantifiable geometric measures and spatial relationships. 

Abstract. Infrastructure built on perennially frozen ice-richground relies heavily on thermally stable subsurface conditions. Climate-warming-induced deepening of ground thaw puts such infrastructure at risk offailure. For better assessing the risk of large-scale future damage to Arcticinfrastructure, improved strategies for model-based approaches are urgentlyneeded. We used the laterally coupled 1D heat conduction model CryoGrid3to simulate permafrost degradation affected by linear infrastructure. Wepresent a case study of a gravel road built on continuous permafrost (Daltonhighway, Alaska) and forced our model under historical and strong futurewarming conditions (following the RCP8.5 scenario). As expected, the presenceof a gravel road in the model leads to higher net heat flux entering theground compared to a reference run without infrastructure and thus a higherrate of thaw. Further, our results suggest that road failure is likely aconsequence of lateral destabilisation due to talik formation in the groundbeside the road rather than a direct consequence of a top-down thawing anddeepening of the active layer below the road centre. In line with previousstudies, we identify enhanced snow accumulation and ponding (both aconsequence of infrastructure presence) as key factors for increased soiltemperatures and road degradation. Using differing horizontal modelresolutions we show that it is possible to capture these key factors and theirimpact on thawing dynamics with a low number of lateral model units,underlining the potential of our model approach for use in pan-Arctic riskassessments. Our results suggest a general two-phase behaviour of permafrost degradation:an initial phase of slow and gradual thaw, followed by a strong increase inthawing rates after the exceedance of a critical ground warming. The timing ofthis transition and the magnitude of thaw rate acceleration differ stronglybetween undisturbed tundra and infrastructure-affected permafrost ground. Ourmodel results suggest that current model-based approaches which do notexplicitly take into account infrastructure in their designs are likely tostrongly underestimate the timing of future Arctic infrastructure failure. By using a laterally coupled 1D model to simulate linearinfrastructure, we infer results in line with outcomes from more complex 2Dand 3D models, but our model's computational efficiency allows us to accountfor long-term climate change impacts on infrastructure from permafrostdegradation. Our model simulations underline that it is crucial to considerclimate warming when planning and constructing infrastructure on permafrost asa transition from a stable to a highly unstable state can well occur withinthe service lifetime (about 30 years) of such a construction. Such atransition can even be triggered in the coming decade by climate change forinfrastructure built on high northern latitude continuous permafrost thatdisplays cold and relatively stable conditions today. 

Abstract. The Earth climate system is out of energy balance, and heat hasaccumulated continuously over the past decades, warming the ocean, the land,the cryosphere, and the atmosphere. According to the Sixth Assessment Reportby Working Group I of the Intergovernmental Panel on Climate Change,this planetary warming over multiple decades is human-driven and results inunprecedented and committed changes to the Earth system, with adverseimpacts for ecosystems and human systems. The Earth heat inventory providesa measure of the Earth energy imbalance (EEI) and allows for quantifyinghow much heat has accumulated in the Earth system, as well as where the heat isstored. Here we show that the Earth system has continued to accumulateheat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to aheating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority,about 89 %, of this heat is stored in the ocean, followed by about 6 %on land, 1 % in the atmosphere, and about 4 % available for meltingthe cryosphere. Over the most recent period (2006–2020), the EEI amounts to0.76±0.2 W m−2. The Earth energy imbalance is the mostfundamental global climate indicator that the scientific community and thepublic can use as the measure of how well the world is doing in the task ofbringing anthropogenic climate change under control. Moreover, thisindicator is highly complementary to other established ones like global meansurface temperature as it represents a robust measure of the rate of climatechange and its future commitment. We call for an implementation of theEarth energy imbalance into the Paris Agreement's Global Stocktake based onbest available science. The Earth heat inventory in this study, updated fromvon Schuckmann et al. (2020), is underpinned by worldwide multidisciplinarycollaboration and demonstrates the critical importance of concertedinternational efforts for climate change monitoring and community-basedrecommendations and we also call for urgently needed actions for enablingcontinuity, archiving, rescuing, and calibrating efforts to assure improvedand long-term monitoring capacity of the global climate observing system. The data for the Earth heat inventory are publicly available, and more details are provided in Table 4.