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Forests overlap with large parts of the northern hemisphere permafrost area, and representing canopy processes is therefore crucial for simulating thermal and hydrological conditions in these regions. Forests impact permafrost through the modulation of radiative fluxes and exchange of turbulent fluxes, precipitation interception and regulation of transpiration. Forests also feature distinct soil layers of litter and organic matter, which play central roles for the infiltration and evaporation of water, while also providing thermal insulation for deeper ground layers. In this study, we present a new module within the CryoGrid community model to simulate forest ecosystems and their impact on the surface water and energy balance. The module includes a big‐leaf vegetation scheme with adaptations for canopy heat storage and transpiration. Furthermore, we account for the effect of surface litter layers on water and energy transfer. We show that the model is capable of simulating radiation, snow cover and ground temperatures below a deciduous needleleaf forest on a north‐facing slope in the Khentii Mountains in Central Mongolia. A sensitivity analysis of topographic aspect and ecosystem configuration confirms the important role of the litter layers for the energy and water balance of the ground. Furthermore, it suggests that the presence of permafrost is primarily linked to topographic aspect rather than the presence of forest at this site. The presented model scheme can be used to study the development of the ground thermal regime in forests, including the state of permafrost, under different climate, ecosystem, and land use scenarios. Plain Language Summary: Large parts of the permafrost in the Northern Hemisphere are covered by forest, which impact the transfer of water and energy between the atmosphere and the ground. In this study we include single‐layer vegetation scheme in an established permafrost model, and test this for a site in Mongolia at the southern limit of Eurasian permafrost and boreal forest. We find that forest soils have smaller variations in ground temperature than on the nearby steppe, and that surface litter layers provide substantial insulation and soil water retention. Our simulations also suggest that the local distribution of permafrost in our study area is linked to topography rather than forest cover. The vegetation scheme is based on a global land surface model and represents a new modeling tool that readily can be used for further studies of permafrost‐ecosystem interactions. Key Points: Novel developments in the CryoGrid Community Model reproduce how forests dampen the seasonal ground surface temperature signalSurface litter layers are essential for forest ground hydrothermal regime as they provide thermal insulation and retain soil moisturePermafrost in our simulations is confined to north‐facing slopes in the study region regardless of ecosystem setup [ABSTRACT FROM AUTHOR]

Extensive regions in the permafrost zone are projected to become climatically unsuitable to sustain permafrost peatlands over the next century, suggesting transformations in these landscapes that can leave large amounts of permafrost carbon vulnerable to post‐thaw decomposition. We present 3 years of eddy covariance measurements of CH4 and CO2 fluxes from the degrading permafrost peatland Iškoras in Northern Norway, which we disaggregate into separate fluxes of palsa, pond, and fen areas using information provided by the dynamic flux footprint in a novel ensemble‐based Bayesian deep neural network framework. The 3‐year mean CO2‐equivalent flux is estimated to be 106 gCO2 m−2 yr−1 for palsas, 1,780 gCO2 m−2 yr−1 for ponds, and −31 gCO2 m−2 yr−1 for fens, indicating that possible palsa degradation to thermokarst ponds would strengthen the local greenhouse gas forcing by a factor of about 17, while transformation into fens would slightly reduce the current local greenhouse gas forcing. Plain Language Summary: Arctic and sub‐arctic regions on the southern border of the permafrost zone often feature peatlands with a patchy surface of peat mounds, thaw ponds, and surrounding fens. As the permafrost underneath peat mounds thaws, these areas transform and can change their emission or uptake of greenhouse gases like CO2 and methane. Assessing this gas exchange on the patchy surface is difficult because our measurement techniques cannot directly observe the variability in space and time. We collected 3 years of gas exchange measurements at a Norwegian permafrost peatland and developed a new method using a collection of uncertainty‐aware neural networks to predict the greenhouse gas exchange of different surface types. Our work suggests that large amounts of methane are emitted by ponds and fens, while the elevated peat mounds have almost no methane emissions. For CO2, we see that ponds are strong emitters, while fens take up substantial amounts as their vegetation absorbs this gas. We are still unsure when the peat mounds will collapse and if they turn into ponds or fens, but we can say that pond formation would give a 17 fold increase in greenhouse gas emissions, while fen formation would slightly reduce today's emissions of permafrost peatlands. Key Points: Eddy covariance fluxes are disaggregated for different surfaces using Bayesian neural networks to derive uncertainty‐aware carbon balancesWhile palsa areas have a near‐zero annual methane balance, the fens and ponds that form upon palsa degradation emit large amounts of methaneFens compensate for methane emissions with strong annual CO2 sinks, while ponds appear as strong, yet uncertain, CO2 emission hotspots [ABSTRACT FROM AUTHOR]

Monitoring the snowpack remains challenging in part due to the limited availability of observations. On the one hand, the deployment of dense ground-based monitoring networks is hampered by logistical hurdles. On the other hand, satellite-based remote sensing products provide only partial information about the snowpack, often limited to snow-covered area or surface temperature. Numerical models are a valuable tool to help fill the gaps in snowpack monitoring. Model performance is nonetheless contingent upon the quality of meteorological forcing, which is often highly uncertain especially in complex terrain. To address these limitations, data assimilation techniques that integrate available observations with snow models have been proposed as a viable option to simultaneously help constrain model uncertainty and add value to observations by improving estimates of the snowpack state. However, the propagation of information from spatially sparse observations in high resolution simulations remains an under-explored topic. To remedy this, the development of data assimilation techniques that can spread information in space is a crucial step. Herein, we examine the potential of spatio-temporal data assimilation for integrating sparse snow depth observations with hyper-resolution (5 m) snow simulations in the Izas central Pyrenean experimental catchment (Spain). Our experiments were developed using the Multiple Snow Data Assimilation System (MuSA) with new improvements to tackle the spatio-temporal data assimilation. Therein, we used a Deterministic Ensemble Smoother with Multiple Data Assimilation (DES-MDA) with domain localization. Three different experiments were performed to showcase capabilities of spatio-temporal information transfer in hyper-resolution snow simulations. Experiment I employed the conventional geographical Euclidean distance to map the similarity between cells. Experiment II utilized the Mahalanobis distance in a multi-dimensional topographic space using terrain parameters extracted from a digital elevation model. Experiment III utilized a more direct mapping of snowpack similarity from a single complete snow depth map together with the easting and northing coordinates. Although all experiments showed a noticeable improvement in the snow patterns in the catchment compared with the deterministic open loop in terms of correlation (r = 0.13) and root-mean-square error (RMSE = 1.11 m), the use of topographical dimensions (Experiment II, r = 0.63 and RMSE = 0.89 m) and observations (Experiments III, r = 0.92 and RMSE = 0.44 m) largely outperform the simulated patterns in Experiment I (r = 0.38 and RMSE = 1.16 m). At the same time, Experiments II & III are considerably more challenging to set up. The results of these experiments can help pave the way for the creation of snow reanalysis and forecasting tools that can seamlessly integrate sparse information from national monitoring networks and high-resolution satellite information. [ABSTRACT FROM AUTHOR]

In many Arctic regions marine coastlines change rapidly in the currently warming climate. In contrast, coastal rock cliffs on Svalbard are considered to be comparably stable, based on previous studies that considered only a few years and limited coastal reaches. Long-term trends of coastal retreat rates in rock cliffs on Svalbard are unknown so far, but their quantification could improve the understanding of coastal dynamics on the Arctic archipelago. This study presents coastal retreat rates in rock cliffs along several kilometers of the Brøgger peninsula, Svalbard. The work is based on high-resolution orthoimages from 1970, 1990, 2010, and 2021, corroborated by high-precision dGNSS measurements along selected segments of the coastline and by rock surface temperature measurements during the period 2020–2021. Our analysis shows that coastal retreat rates accelerate statistically significant along the Brøgger peninsula in the time period of 2010 to 2021. This is true for both the northeast facing coastline, with retreat rates increasing from 0.04 ± 0.06 m/a (1970–1990) and 0.04 ± 0.04 m/a (1990–2010) to 0.07 ± 0.08 m/a (2010–2021) and the southwest facing coastline, where retreat rates of 0.26 ± 0.06 m/a (1970–1990), 0.24 ± 0.04 m/a (1990–2010) and 0.30 ± 0.08 m/a (2010–2021) are measured. Furthermore, the parts of the coastline affected by erosion increase along the northeast facing coastline from 47 % (1970–1990) to 65 % (2010–2021), while they stay consistently above 90 % along the southwest facing coastline. Measurements of rock surface temperature show mean annual values close to the thaw threshold with −0.49 °C at the southwest facing coastline, while records at the northeast facing coastline are lower with −1.64 °C. The recently accelerated retreat rates coincide with increasing storminess and retreating sea ice, together with increasing ground temperatures, all factors that can enhance coastal erosion. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open source software dedicated to snow data assimilation. Here we introduce a new data assimilation toolbox, the Multiscale Snow Data Assimilation System (MuSA). MuSA was developed to fuse remotely sensed information with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable. It enables assimilation of different state variables such as the snow depth, SWE, snow surface temperature, binary or fractional snow-covered area, and snow albedo and could be easily upgraded to assimilate other variables such as liquid water content or snow density in the future. MuSA allows the joint assimilation of an arbitrary number of these variables, through the generation of an ensemble of FSM2 simulations. The characteristics of the ensemble (i.e. the number of particles and their covariance) may be controlled by the user, and it is generated by perturbing the meteorological forcing of FSM2. The observational variables may be assimilated using different algorithms including the particle filters and smoothers as well as ensemble Kalman filters and smoothers along with their iterative variants. We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (Central Pyrenees). Furthermore, we conducted a joint assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single cell experiment. In light of these experiments, we discuss the pros and cons of assimilation algorithms, including their computational cost. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

This study describes and evaluates a new downscaling scheme that specifically addresses the need for hillslope scale atmospheric forcing time-series for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps. It achieves this by utilising the previously published TopoSCALE scheme (Fiddes et al. 2014) to generate a synthetic observation of current climate at hillslope scale while accounting for a broad range of surface-atmosphere interactions. These synthetic observations are then used to debias (downscale) CORDEX climate variables using the quantile mapping method. A further temporal disaggregation step produces sub-daily fields. This approach has the advantages of other empirical-statistical methods, namely speed of use while avoiding the need for ground data, which is often limited. It is therefore a suitable method for a wide range of remote regions where ground data is absent, incomplete, or not of sufficient length. The approach is evaluated using a network of high elevation stations across the Swiss Alps and a test application of modelling climate change impacts on Alpine snow cover is given. [ABSTRACT FROM AUTHOR]

The snowpack over the Mediterranean mountains constitutes a key water resource for the downstream populations. However, its dynamics have not been studied in detail yet in many areas, mostly because of the scarcity of snowpack observations. In this work, we present a characterization of the snowpack over the two mountain ranges of Lebanon. To obtain the necessary snowpack information, we have developed a 1 km regional scale snow reanalysis (ICAR_assim) covering the period 2010-2017. ICAR_assim was developed by means of ensemble-based data assimilation of MODIS fractional snow-covered area (fSCA) through the energy and mass balance model the Flexible Snow Model (FSM2), using the Particle Batch Smoother (PBS). The meteorological forcing data was obtained by a regional atmospheric simulation developed through the Intermediate Complexity Atmospheric Research model (ICAR) nested inside a coarser regional simulation developed by the Weather Research and Forecasting model (WRF). The boundary and initial conditions of WRF were provided by the ERA5 atmospheric reanalysis. ICAR_assim showed very good agreement with MODIS gap-filled snow products, with a spatial correlation of R = 0.98 in the snow probability (P(snow)), and a temporal correlation of R = 0.88 in the day of peak snow water equivalent (SWE)Similarly, ICAR_assim has shown a correlation with the seasonal mean SWE of R = 0.75 compared with in-situ observations from Automatic Weather Stations (AWS). The results highlight the high temporal variability of the snowpack in the Lebanon ranges, with differences between Mount Lebanon and Anti-Lebanon that cannot be only explained by its hypsography been Anti-Lebanon in the rain shadow of Mount Lebanon. The maximum fresh water stored in the snowpack is in the middle elevations approximately between 2200 and 2500 m. a.s.l. Thus, the resilience to further warming is low for the snow water resources of Lebanon due to the proximity of the snowpack to the zero isotherm. [ABSTRACT FROM AUTHOR]