Abstract. The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice/water balance for permafrost and glaciers, extending a well-established suite of permafrost models (CryoGrid 1, 2 and 3). The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming. Different model components, characterized by their process representations and parametrizations, are realized as classes (i.e. objects) in CryoGrid. Standardized communication protocols between these classes ensure that they can be stacked vertically. For example, the CryoGrid community model features several classes with different complexity for the seasonal snow cover which can be flexibly combined with a range of classes representing subsurface materials, each with their own set of process representations (e.g. soil with and without water balance, glacier ice). We present the CryoGrid architecture as well as the model physics and defining equations for the different model classes, focusing on one-dimensional model configurations which can also interact with external heat and water reservoirs. We illustrate the wide variety of simulation capabilities for a site on Svalbard, with point-scale permafrost simulations using e.g. different soil freezing characteristics, drainage regimes and snow representations, as well as simulations for glacier mass balance and a shallow water body. The CryoGrid community model is not intended as a static model framework, but aims to provide developers with a flexible platform for efficient model development. In this study, we document both basic and advanced model functionalities to provide a baseline for the future development of novel cryosphere models.

Abstract. Subarctic peatlands underlain by permafrost contain significant amounts of organic carbon. Our ability to quantify the evolution of such permafrost landscapes in numerical models is critical for providing robust predictions of the environmental and climatic changes to come. Yet, the accuracy of large-scale predictions has so far been hampered by small-scale physical processes that create a high spatial variability of thermal surface conditions, affecting the ground thermal regime and thus permafrost degradation patterns. In this regard, a better understanding of the small-scale interplay between microtopography and lateral fluxes of heat, water and snow can be achieved by field monitoring and process-based numerical modeling. Here, we quantify the topographic changes of the Šuoššjávri peat plateau (northern Norway) over a three-year period using drone-based repeat high-resolution photogrammetry. Our results show thermokarst degradation is concentrated on the edges of the plateau, representing 77 % of observed subsidence, while most of the inner plateau surface exhibits no detectable subsidence. Based on detailed investigation of eight zones of the plateau edge, we show that this edge degradation corresponds to an annual volume change of 0.13±0.07 m3 yr−1 per meter of retreating edge (orthogonal to the retreat direction). Using the CryoGrid3 land surface model, we show that these degradation patterns can be reproduced in a modeling framework that implements lateral redistribution of snow, subsurface water and heat, as well as ground subsidence due to melting of excess ice. By performing a sensitivity test for snow depths on the plateau under steady-state climate forcing, we obtain a threshold behavior for the start of edge degradation. Small snow depth variations (from 0 to 30 cm) result in highly different degradation behavior, from stability to fast degradation. For plateau snow depths in the range of field measurements, the simulated annual volume changes are broadly in agreement with the results of the drone survey. As snow depths are clearly correlated with ground surface temperatures, our results indicate that the approach can potentially be used to simulate climate-driven dynamics of edge degradation observed at our study site and other peat plateaus worldwide. Thus, the model approach represents a first step towards simulating climate-driven landscape development through thermokarst in permafrost peatlands.

Background: Climate change is a major global challenge, especially for Indigenous communities. It can have extensive impacts on peoples' lives that may occur through the living environment, health and mental well-being, and which are requiring constant adaptation. Objectives: The overall purpose of this research was to evaluate the impacts of climate change and permafrost thaw on mental wellness in Disko Bay, Greenland. It contained two parts: multidisciplinary fieldwork and a questionnaire survey. The aim of the fieldwork was to learn about life and living conditions and to understand what it is like to live in a community that faces impacts of climate change and permafrost thaw. For the questionnaire the aim was to find out which perceived environmental and adaptation factors relate to very good self-rated wellbeing, quality of life and satisfaction with life. Analysis: Fieldwork data was analyzed by following a thematic analysis, and questionnaire data statistically by cross-tabulation. First, the associations between perceived environmental and adaptation factors were studied either by the Pearson χ 2 test or by Fisher's exact test. Second, binary logistic regression analysis was applied to examine more in depth the associations between perceived environmental/adaptation variables and self-rated very good well-being, satisfaction with life and quality of life. The binary logistic regression analysis was conducted in two phases: as univariate and multivariate analyses. Results: Nature and different activities in nature were found to be important to local people, and results suggest that they increase mental wellness, specifically well-being and satisfaction with life. Challenges associated with permafrost thaw, such as changes in the physical environment, infrastructure and impacts on culture were recognized in everyday life. Conclusions: The results offer relevant information for further plans and actions in this field of research and at the policy level. Our study shows the importance of multidisciplinary research which includes the voice of local communities.

Abstract. The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice–water balance for permafrost and glaciers, extending a well-established suite of permafrost models (CryoGrid  1, 2, and 3). The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming. Different model components, characterized by their process representations and parameterizations, are realized as classes (i.e., objects) in CryoGrid. Standardized communication protocols between these classes ensure that they can be stacked vertically. For example, the CryoGrid community model features several classes with different complexity for the seasonal snow cover, which can be flexibly combined with a range of classes representing subsurface materials, each with their own set of process representations (e.g., soil with and without water balance, glacier ice). We present the CryoGrid architecture as well as the model physics and defining equations for the different model classes, focusing on one-dimensional model configurations which can also interact with external heat and water reservoirs. We illustrate the wide variety of simulation capabilities for a site on Svalbard, with point-scale permafrost simulations using, e.g., different soil freezing characteristics, drainage regimes, and snow representations, as well as simulations for glacier mass balance and a shallow water body. The CryoGrid community model is not intended as a static model framework but aims to provide developers with a flexible platform for efficient model development. In this study, we document both basic and advanced model functionalities to provide a baseline for the future development of novel cryosphere models.

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Thawing permafrost creates risks to the environment, economy and culture in Arctic coastal communities. Identification of these risks and the inclusion of the societal context and the relevant stakeholder involvement is crucial in risk management and for future sustainability, yet the dual dimensions of risk and risk perception is often ignored in conceptual risk frameworks. In this paper we present a risk framework for Arctic coastal communities. Our framework builds on the notion of the dual dimensions of risk, as both physically and socially constructed, and it places risk perception and the coproduction of risk management with local stakeholders as central components into the model. Central to our framework is the importance of multidisciplinary collaboration. A conceptual model and processual framework with a description of successive steps is developed to facilitate the identification of risks of thawing permafrost in a collaboration between local communities and scientists. Our conceptual framework motivates coproduction of risk management with locals in the identification of these risks from permafrost thaw and the development of adaptation and mitigation strategies.

Supplement 1: Quick start for CryoGrid community model Software requirements: CryoGrid is written in Matlab, version 2018 or higher is required. For parallel applications, the Matlab parallel toolbox is required. 1.1 Download and set up file structure a) select or create a folder for the model code and results, e.g. "CryoGrid_git" -download zip-file with CryoGrid source code, CryoGridCommunity_source.zip, from https://doi.org/10.5281/zenodo.6522424 and unpack in "CryoGrid_git".b) download the zip-file with run files to be modified by the user, "CryoGridCommunity_run.zip", from https://doi.org/10.5281/zenodo.6522424 and unpack unpack in "CryoGrid_git".c) "CryoGrid_git" should now contain the subfolders "CryoGrid_git/CryoGridCommunity_source" and "CryoGrid_git/CryoGridCommunity_run", and "CryoGrid_git/CryoGridCommunity_source" the subfolders "source" and "UMLs", while "CryoGrid_git/CryoGridCommunity_run" should contain the file "run_CG.m" (in addition to other files and folders).d) Create a folder for parameter files and simulation results. For the default setting, create a folder "CryoGridCommunity_results" in "CryoGrid_git", which should look like this:1.2 Set up parameter files -using existing parameter files from Supplement 4 (recommended as first step) download and unpack the file "CryoGridCommunity_parameter_files.zip" from https://doi.org/10.5281/zenodo.6522424.Copy the herein contained folders (i.e. "forcing", "glacier", etc.) in the folder "CryoGrid_git/CryoGridCommunity_results" (see Suppl. 1.1 above). Each of the folders represents a simulation from Sect. 3.2, with folders named according to the run names (see Suppl. 4). The common model forcing is provided in the folder "forcing".-use automatic parameter file creator (recommended for advanced users) a) edit and run the script "create_parameter_file_EXCEL.m" in "CryoGrid_git/ CryoGridCommunity_run". A list of class names, their index and class options (for some classes) must be provided in the header of the file, as well as the folder in which he parameter files and results are stored (in this case "../CryoGridCommunity_results"). The script generates a parameter file

Abstract. Subarctic peatlands underlain by permafrost contain significant amounts of organic carbon and our ability to quantify the evolution of such permafrost landscapes in numerical models is critical to provide robust predictions of the environmental and climatic changes to come. Yet, the accuracy of large-scale predictions is so far hampered by small-scale physical processes that create a high spatial variability of surface ground thermal regime and thus of permafrost degradation patterns. In this regard, a better understanding of the small-scale interplay between microtopography and lateral fluxes of heat, water and snow can be achieved by field monitoring and process-based numerical modeling. Here, we quantify the topographic changes of the Šuoššjávri peat plateau (Northern Norway) over a three-years period using repeated drone-based high-resolution photogrammetry. Our results show that edge degradation is the main process through which thermal erosion occurs and represents about 80 % of measured subsidence, while most of the inner plateau surface exhibits no detectable subsidence. Based on detailed investigation of eight zones of the plateau edge, we show that this edge degradation corresponds to a volumetric loss of 0.13 ± 0.07 m3 yr−1 m−1 (cubic meter per year and per meter of plateau circumference). Using the CryoGrid land surface model, we show that these degradation patterns can be reproduced in a modeling framework that implements lateral redistribution of snow, subsurface water and heat, as well as ground subsidence due to melting of excess ice. We reproduce prolonged climate-driven edge degradation that is consistent with field observations and present a sensitivity test of the plateau degradation on snow depth over the plateau. Small snow depth variations (from 0 to 30 cm) result in highly different degradation behavior, from stability to fast degradation. These results represent a new step in the modeling of climate-driven landscape development and permafrost degradation in highly heterogeneous landscapes such as peat plateaus. Our approach provides a physically based quantification of permafrost thaw with a new level of realism, notably, regarding feedback mechanisms between the dynamical topography and the lateral fluxes through which a small modification of the snow depth result in dramatic modifications of the permafrost degradation intensity. In this regard, these results also highlight the major control of snow pack characteristics on the ground thermal regime and the potential improvement that accurate snow representation and prediction could bring to projections of permafrost degradation.