Warming ground temperatures in the Arctic raise the need to forecast permafrost thaw. Seasonal snow cover is a crucial factor for ground temperatures as it can have a warming or cooling effect on the underlying soil, depending on snow cover timing and its physical properties. Vegetation and topography modulate snow distribution and affect the snow thermal insulation. However, the formation processes and resulting properties of Arctic snowpacks are difficult to represent in snow models and in-situ data is sparse. Further understanding of the interactions between snow, vegetation and permafrost and the deduction of empirical relationships could support the parametrization of snow in permafrost modeling.We study how the ground thermal regime is influenced by the interplay of snow, vegetation, topography and climatic conditions. In particular, we evaluate the effect of snow density variation on the ground thermal regime. We present a novel dataset that combines air, surface and soil temperature, as well as soil moisture time series recorded from September 2024 to August 2025 with end-of-season snow depth distribution and high-resolution vertical snow density profiles. Temperatures and soil moisture were monitored using 60 TOMST TMS-4 loggers, distributed across different vegetation types and topographic features in the taiga-tundra ecotone (Trail Valley Creek, Northwest Territories, Canada). Snow density profiles were measured in March 2025 next to the TOMST loggers using a SnowMicroPen.Our data shows several characteristic snowpack types which do not only differ in depth but also have a different layering structure. Low density snowpacks with high depth hoar fractions are most prominent in forested areas that are shielded from the wind, whereas leeward slopes can accumulate thick, high-density wind slab, regardless of vegetation. While snow depth is clearly one of the major drivers of soil temperature, the role of snow density is more complex.Categorization of different tundra vegetation types with characteristic snow conditions and specific impact on permafrost vulnerability helps to refine permafrost models and constrain predictions of permafrost thaw.

Abstract. Arctic snow exerts a critical control on winter soil temperature and carbon exchange, however representation of its properties in Earth System Models (ESMs) remains simplified. In the Community Land Model v5.0 (CLM5.0), recent updates to snow compaction schemes have led to overly dense tundra snow, increasing snow thermal conductivity and conductive heat loss, producing a persistent cold-soil bias. Here we developed a Random Forest (RF) regression model to derive tundra bulk snow density from meteorological variables, trained on six winter seasons (Sep – May) of Arctic SVS2-Crocus (ASC) simulations constrained by in-situ observations from Trail Valley Creek (TVC), Northwest Territories, Canada. RF-derived snow densities capture the lower bulk densities of Arctic tundra snowpacks, which CLM5.0’s compaction scheme overestimates. Application of the RF-derived snow densities to CLM5.0 reduces snow thermal conductivity and enhances snowpack insulation, decreasing soil temperature RMSE by approximately 2–3 °C relative to field measurements (2017–2023) and increasing future winter soil temperature projections (2016–2100) by 4–7 °C under RCP4.5 and 8.5. Such increases in simulated soil temperature highlight the strong sensitivity of CLM5.0’s soil thermal regime to snow physical properties. The RF model reproduces ASC-simulated density evolution with a mean absolute error of 32 kg m−3 which falls within typical measurement error for volumetric snow density sampling in Arctic tundra (20–40) kg m−3 and matches field measurements more closely than default CLM5.0. Future bulk snow density predictions using the RF model driven by bias-corrected North American-Coordinated Regional Downscaling Experiment (NA-CORDEX) meteorology indicate bulk snow densities 200–450 kg m−3 lower than CLM5.0 and more consistent with tundra conditions.

Sophisticated snowpack models are required to provide accurate estimation of snowpack properties across the forest-tundra ecotone where in situ measurements are sparse. As snowpack properties strongly influence radar scattering signals, accurate simulation is crucial for the success of spaceborne radar missions to retrieve snow water equivalent (SWE). In this study, we evaluate the ability of default and Arctic versions of Crocus embedded within the Soil, Vegetation and Snow version 2 (SVS2-Crocus) land surface model to simulate snowpack properties (e.g. depth, density, SWE, specific surface area; SSA) across a 40 km transect of the Northwest Territories, Canada, using two winter seasons (2021-22 and 2022-23) of in situ measurements. An ensemble of simulated snowpack properties (120 members from default and Arctic SVS2-Crocus) was used in the Snow Microwave Radiative Transfer (SMRT) model to simulate Ku-band (13.5 GHz) backscatter. Simulated backscatter from multi-layer (similar to 20-layer) SVS2-Crocus snowpack simulations and simplified 3-layer "radar-equivalent snowpack" SVS2-Crocus simulations were compared to simulated backscatter from snow pit observations (with no snow layer simplification). Results highlight that Arctic SVS2-Crocus wind-induced compaction modifications were spatially transferable across the forest-tundra ecotone, reducing the RMSD of surface density by an average of 29 %. Basal vegetation modifications were less effective in simulating low-density basal snow layers at all sites (2022 and 2023; default RMSD: 67 kg m(-3); Arctic RMSD: 69 kg m(-3)) but were necessary to simulate a physically representative Arctic density profile. SVS2-Crocus underestimated SSA leading to high errors in the simulation of snow backscatter (2022 and 2023; default RMSD 3.5 dB; Arctic RMSD: 4.8 dB). RMSD of backscatter was reduced by implementing a minimum SSA value (8.7 m(2) kg(-1); 2022 and 2023; default RMSD: 1.5 dB; Arctic RMSD: 1.5 dB). A radar-equivalent snowpack was effective in retaining the scattering behaviour of the multi-layer snowpack (RMSD < 1 dB) providing a means to estimate SWE with increased computational efficiency.

Snow is a potent insulator, influencing the temperature of the active layer and the permafrost in the Arctic region. However, our understanding of spatial patterns of snow properties and their interplay with vegetation remains limited due to scarcity of local and regional snow data. Furthermore, the duration, depth, and physical properties of the Arctic snow cover are changing with rising air temperature and new precipitation patterns. We study the spatial snow distribution and its drivers and consequences around the Trail Valley Creek research catchment in the Northwest Territories, Canada. Our dataset includes a 143 km² snow depth raster captured on April 2, 2023, at a 1-meter spatial resolution, as well as data from 13 spatially distributed loggers measuring air/snow temperature, soil surface temperature, and soil temperature at 8 cm depth from August 27, 2022, to August 9, 2023. Detailed information on vegetation types, structure, and soil properties at all locations is included. Our analysis covers the timing of soil freeze and thaw, snow and soil temperatures, and their correlation with vegetation characteristics, particularly focusing on April snow depth. Our findings underscore the pivotal role of snow in regulating soil temperature, making it a key driver for permafrost protection or thaw. The results reveal significant variability in April snow depth across the 13 study locations, ranging from no snow to 1.7 meters, resulting in winter minimum soil temperatures between -31°C and -4°C. The study confirms that thicker snow cover contributes to warmer soil temperatures. While the soil at 8 cm freezes uniformly in mid-October across all sites, snow patterns lead to high variability in soil thawing dates, which span one month between May 10 and June 08, 2023. Understanding the spatial patterns of snow depth, thermal properties, and timing is crucial for assessing the snow effect on soil temperature. The large range of winter soil temperatures, which we observed, may lead to differences in thaw depth development in the following summer and potentially to talik formation affecting permafrost stability.

Abstract. Winter carbon dioxide (CO2) and methane (CH4) fluxes from soils under seasonal snowpacks make non-negligible, yet poorly constrained contributions to annual carbon budgets across Arctic regions. Quantifying these fluxes and their spatial variance will better constrain uncertainties in simulations of winter carbon fluxes from terrestrial biosphere models. We address this gap by measuring and identifying patterns and spatial variability in CO2 and CH4 soil-atmosphere fluxes through late winter snowpacks at an upland tundra site in the western Canadian Arctic. Instantaneous fluxes were calculated from CO2 and CH4 microsite concentration gradients at 10 cm to 20 cm vertical resolution (n = 119) through the snowpack across five homogeneous surface covers, representing dominant vegetation types. We measured consistent soil-to-atmosphere CO2 fluxes but with significantly different rates across surface covers (0.8 to 100 mgC m-2 day-1), which were strongly influenced by snow depth and soil surface temperature, exhibiting higher emissions under deeper snowpacks and warmer soil surfaces. CH4 fluxes were also coupled to soil surface temperature and varied between −0.04 and 0.08 mgC m-2 day-1. Persistent CH4 uptake was observed in warmer soils (-6.0 to -0.5 °C) in a sparsely populated black spruce and shrub dominated area with deep snow, indicating active methane oxidation during winter. Microsite scale CO2 and CH4 fluxes were statistically independent of vertical snow microstructure, indicating that winter fluxes could be reliably calculated from single gas concentration at the soil-snow interface, negating the need for additional snowpack gas measurements. These results open new avenues for quantifying fine-scale spatial variability of wintertime CO2 and CH4 fluxes in Arctic tundra, which can constrain biogeochemical process representations in terrestrial biosphere models and inform spatial upscaling methodologies.

Multiple options for representing physical processes in forest canopies are added to FSM, which is a model with multiple options for representing physical processes in snow on the ground. The canopy processes represented are shortwave and longwave radiative transfer; turbulent transfers of heat and moisture; and interception, sublimation, unloading, and melt of snow in the canopy. There are options for Beer's law or two-stream approximation canopy radiative transfer, linear or non-linear canopy snow interception efficiency, and time- and melt-dependent or temperature- and wind-dependent canopy snow unloading. Canopy mass and energy balance equations can be solved with one or two model layers. Model behaviour on stand scales is compared with observations of above- and below-canopy shortwave and longwave radiation, below-canopy wind speed, snow mass on the ground, and subjective estimates of canopy snow load. Large-scale simulations of snow cover extent, snow mass, and albedo for the Northern Hemisphere are compared with observations and land-only simulations by state-of-the-art Earth system models. Without accounting for uncertainty in forest structure metrics and parameter values, the ranges of multi-physics ensemble simulations are not as wide as seen in intercomparisons of existing models. FSM2 provides a platform for rapid investigation of sensitivity to model structure and parameter values or ensemble-based data assimilation for snow in open and forested environments.

The precise magnitude and timing of permafrost-thaw-related emissions and their subsequent impact on the global climate system remain highly uncertain. This uncertainty stems from the complex quantification of the rate and extent of permafrost thaw, which is influenced by factors such as snow cover and other surface properties. Acting as a thermal insulator, snow cover directly influences surface energy fluxes and can significantly impact the permafrost thermal regime. However, current Earth system models often inadequately represent the nuanced effects of snow cover in permafrost regions, leading to inaccuracies in simulating soil temperatures and permafrost dynamics. Notably, the Community Land Model (CLM5.0) tends to overestimate snowpack thermal conductivity over permafrost regions, resulting in an underestimation of the snow insulating capacity. Using a snow thermal conductivity scheme better adapted for the snowpack typically found in permafrost regions, we seek to resolve thermal insulation underestimation and assess the influence of snow on simulated soil temperatures and permafrost dynamics. Evaluation using two Arctic-wide soil temperature observation datasets reveals that the new snow thermal conductivity scheme reduces the cold-soil temperature bias (root-mean-square error, RMSE = 3.17 to 2.4 °C, using remote sensing data; RMSE = 3.9 to 2.19 °C, using in situ data), demonstrates robustness through sensitivity analysis under lower tundra snow densities, and addresses the overestimation of permafrost extent in the default CLM5.0. This improvement highlights the importance of incorporating realistic snow processes in land surface models for enhanced predictions of permafrost dynamics and their response to climate change.

Around 22% of the global population depend on mountain runoff for their water supply. Due to its importance for future water resources, as well as flood and drought planning, an improved understanding of spatial precipitation patterns in mountain regions is needed. Precipitation gauge networks are sparse and traditional methods of interpolation yield inadequate precipitation fields for poorly gauged mountain catchments. This research project builds on a new method, Random Mixing, to generate multiple random spatial daily precipitation fields, conditioned on gauge observations. The Random Mixing algorithm has so far been tested on larger, densely gauged catchments. This project adapts the approach for a sparsely gauged, small 9.1 km2 mountain catchment, Marmot Creek Research Basin in Alberta, Canada, where elevations range between 1600 m and 2825 m above sea level (a.s.l.). Quality-controlled total precipitation (i.e., rainfall and snowfall) gauge observations, for an 11-year period, from three weather stations around the catchment have been used to condition the random spatial fields. Three modifications have been made to the Random Mixing method: improving spatial covariance, introducing elevation dependence and evaluating seasonal effects. Leave-one-out cross-validation is used, comparing spatial fields from the new method with other spatial interpolation techniques, including Inverse Distance Weighting and Kriging with External Drift. Results are promising: even with very few gauges, improving the way that spatial covariance relationships between gauge locations are represented in the model has enhanced the quality of the spatial fields. To optimise selection of the most plausible fields, ensemble hydrological simulations are run, using a modified version of the HBV spatially-distributed conceptual model, and the physically-based Cold Regions Hydrological Model (CRHM), with spatial precipitation fields generated on a 50 m2 regular model grid. Optimisation involves the use of metrics, primarily Nash-Sutcliffe Efficiency (NSE) and bias, to identify the fields that result in the best match between observed and simulated streamflows. Outputs from HBV and CRHM ensemble simulations are compared to evaluate the impact of model structure on catchment response and spatial precipitation field optimisation.

Snow microstructure – characterized by density, grain size, grain shape and arrangement – fundamentally determines snowpack macroscopic properties. Despite this critical role, many land surface models (LSMs) lack explicit representation of snow microstructure. This limitation has become increasingly critical as future spaceborne missions for snow water equivalent measurement demand advanced modelling systems capable of accurately estimating snowpack properties, including microstructure, across diverse climatic and vegetation regions. The Soil Vegetation and Snow (SVS) LSM, used by Environment and Climate Change Canada for operational land surface and hydrological predictions, has been substantially upgraded to address these challenges. SVS version 2.0 (SVS2) incorporates the detailed multilayer Crocus snowpack model, enabling distinct simulations of snowpack evolution in both open terrain and forested areas within each grid cell. Crocus within SVS2 has been upgraded from its original alpine design with three major enhancements to handle Canada's varied snowpack conditions: an advanced albedo parameterization that accounts for spatial variability in light-absorbing particle deposition, new physical parameterizations tailored to Arctic snow characteristics, and a refined canopy model for forest environments. Significant improvements in simulations of near-surface density predictions are evident along a latitudinal transect from southern Quebec to the Canadian Arctic, while challenges remain in simulation of density and specific surface area in basal snow layers. SVS2 achieved substantial gains in snow melt-out timing accuracy, reducing prediction errors by over 50 % compared to the alpine Crocus version and surpassing two established snow reference products (ERA5-Land and ERA5-Crocus). These enhancements position SVS2 as a substantial advancement for future operational snow modeling applications across Canada.

Snow cover plays a pivotal role in the Arctic's climate, hydrology, and ecology, making the understanding of its deposition and accumulation dynamics crucial. Snow depth and its duration can directly influence soil temperature: the insulating properties of snow increase with greater snow depth, which prevents soil temperatures from declining in winter. Trail Valley Creek, NWT, Canada, is located at the northern boundary of the tundra-taiga transition zone, approximately 45 km north of Inuvik, and is underlain by continuous permafrost. The region’s rapid warming points to a trend of vegetation changes such as shrub expansion northwards into the tundra.Topography and vegetation cover are the main drivers of spatial variation of snow depth across different landscapes, while wind significantly influences snow redistribution. This reallocation causes snow to accumulate preferably in terrain features such as valleys and leeward sides of ridges, and taller vegetation, as their height and intricate structure can favour snow trapping. Understanding the relationships among snow distribution, topography features, and vegetation types is vital, though it is often limited by the scarcity of high-resolution data with broad spatial cover.To investigate the spatial snow distribution in Trail Valley Creek, we analyzed how snow depth varies according to different topography classes and slope aspects, as well as the region’s different vegetation classes and heights. For this purpose, we explored records from Aerial Laser Scanning (ALS) collected during both winter and summer of 2023, covering an area of over 170 km2. We generated a high-resolution Digital Elevation Model (DEM) from the winter snow-covered surface (2023-04-02), a Digital Terrain Model (DTM) from the summer snow-free terrain (2023-07-10), and by combining both, created a 1-m resolution snow depth map of the area. Additionally, we used 3129 Magnaprobe ground-based snow depth measurements for validation (2023-03-26 to 2023-03-29). For the topography analysis, we classified the slope aspects, and subdivided the terrain into 10 geomorphological classes using the geomorphons approach. This method calculates terrain forms, such as plateaus, slopes, ridges and valleys, and their associated geometry using a machine vision approach. To analyze the role of vegetation cover, we used a 13-class map that categorizes land-cover features and vegetation types, such as graminoids, shrubs and trees, and vegetation height rasters, derived from the ALS summer data.Snow is the main driver of the hydrological system in Trail Valley Creek, and the outcomes of this study will provide insights in the important interplay between vegetation, snow depth and terrain characteristics in a permafrost landscape.

The Arctic winter is disproportionately vulnerable to climate warming and approximately 1700 Gt of carbon stored in high-latitude permafrost ecosystems is at risk of degradation in the future due to enhanced microbial activity. Few studies have been directed at high-latitude cold season land-atmosphere processes and it is suggested that the contribution of winter season greenhouse gas (GHG) fluxes to the annual carbon budget may have been underestimated. Snow, acting as a thermal blanket, influences the Arctic soil temperatures during winter and parameters such as snow effective thermal conductivity (Keff) are not well constrained in land surface models, which impacts our ability to accurately simulate wintertime soil carbon emissions. To address this, we investigated the impacts of implementing a Keff parameterisation more suitable to Arctic snowpacks into the Community Land Model (CLM5.0). A point-model version of CLM5.0 forced by an ensemble of NA-CORDEX (North American Coordinated Regional Downscaling Experiment) future climate realisations (RCP 4.5 and 8.5) indicates that median winter CO2 emissions will have more than tripled by the end of the century (2066-2096) under RCP 8.5. Implementing the refined Keff parameterisation increases simulated winter CO2 in the latter half of the century (2066-2096) by 130 % and CH4 flux by 50 % under RCP 8.5 compared to the widely used default Keff parameterisation. The influence of snow Keff parameterisation within CLM5.0 on future simulated CO2 and CH4 is at least as significant, if not more so, than climate variability from a range of NA-CORDEX projections to 2100. The average difference in refined Keff compared with the default Keff raises minimum winter soil temperatures by 4-7 degrees C by the end of the century under RCP 4.5 and 8.5. Furthermore, CLM5.0 simulations using the refined Keff show an extension of the early winter (September-October) zero-curtain period, by nearly a month under RCP 8.5. Consequently, recent increases in both zero-curtain duration and winter CO2 emissions appear set to continue to 2100. Modelled winter soil temperatures and carbon emissions further highlight the importance of climate mitigation in preventing a significant increase in winter carbon emissions from the Arctic in the future.

Abstract. Sea ice thickness is essential for climate studies and numerical weather prediction. Radar altimetry has provided sea ice thickness measurement since the launch of ERS-1 and currently through CryoSat-2, Sentinel-3 and Altika but uncertainty in the scattering horizon used to retrieve sea ice thickness arises from interactions between the emitted signal and snow cover on the ice surface. Therefore, modelling the scattering of the electromagnetic waves with the snowpack and ice is necessary to retrieve the sea ice thickness accurately. The Snow Microwave Radiative Transfer (SMRT) model was used to simulate the low resolution altimeter waveform echo from the snow-covered sea ice, using in-situ measurements as input. Measurement from four field campaigns were used: Cambridge Bay, Eureka Sound and near Alert, Nunavut, Canada in April 2022 in the cold and later winter condition when snow and ice thickness are neat their seasonal maxima prior to melt. In-situ measurements included snow temperature, salinity, density, specific surface area, microstructure from X-ray tomography and surface roughness measurements using structure from motion photogrammetry. Evaluation of SMRT in altimeter mode was performed against CryoSat-2 waveform data in pseudo-low-resolution mode. Simulated and observed waveforms showed good agreement, although it was necessary to adjust sea ice roughness. The retrieved roughness (root-mean-square height) in Cambridge Bay was 2.1 mm and 1.6 mm in Eureka, which was close to the observed value of 1.4 mm for flat sea ice. In addition, simulations of backscatter in preparation for the European Space Agency's CRISTAL mission demonstrated the dominance of scattering from the snow surface at Ku and Ka-band. However, these findings depend on the parameterisation of the roughness. The scattering from the snow surface dominates when roughness is high, but the interface return dominates if the roughness is low ( < 2.5 mm). This is the first study to consider scattering within the snow and demonstrate the origin of CryoSat-2 signals. This work paved the way to a new physical retracker using SMRT to retrieve snow depth and sea ice thickness for radar altimeter missions.

The Arctic poses many challenges for Earth system and snow physics models, which are commonly unable to simulate crucial Arctic snowpack processes,such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost, and the global carbon budget. Recognizing that models are shaped by human choices, 18 Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack but reflected the broader research context. Inadequate and insufficient resources, partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities - an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development - and anchored in the past. This anchoring and non-epistemic values led to diverging opinions about whether existing models were "good enough" and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations currently impeding progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice.

Sophisticated snowpack models such as Crocus and SNOWPACK struggle to properly simulate profiles of density and specific surface area (SSA) within Arctic snowpacks due to underestimation of wind-induced compaction, misrepresentation of basal vegetation influencing compaction and metamorphism, and omission of water vapour flux transport. To improve the simulation of profiles of density and SSA, parameterisations of snow physical processes that consider the effect of high wind speeds, the presence of basal vegetation, and alternate thermal conductivity formulations were implemented into an ensemble version of the Soil, Vegetation, and Snow version 2 (SVS2-Crocus) land surface model, creating Arctic SVS2-Crocus. The ensemble versions of the default and Arctic SVS2-Crocus were driven with in situ meteorological data and evaluated using measurements of snowpack properties (snow water equivalent, SWE; depth; density; and SSA) at Trail Valley Creek (TVC), Northwest Territories, Canada, over 32 years (1991-2023). Results show that both the default and Arctic SVS2-Crocus can simulate the correct magnitude of SWE (root-mean-square error, RMSE, for both ensembles - 55 kg m-2) and snow depth (default RMSE - 0.22 m; Arctic RMSE - 0.18 m) at TVC in comparison to measurements. Wind-induced compaction within Arctic SVS2-Crocus effectively compacts the surface layers of the snowpack, increasing the density, and reducing the RMSE by 41 % (176 kg m-3 to 103 kg m-3). Parameterisations of basal vegetation are less effective in reducing compaction of basal snow layers (default RMSE - 67 kg m-3; Arctic RMSE - 65 kg m-3), reaffirming the need to consider water vapour flux transport for simulation of low-density basal layers. The top 100 ensemble members of Arctic SVS2-Crocus produced lower continuous ranked probability scores (CRPS) than the default SVS2-Crocus when simulating snow density profiles. The top-performing members of the Arctic SVS2-Crocus ensemble featured modifications that raise wind speeds to increase compaction in snow surface layers and to prevent snowdrift and increase viscosity in basal layers. Selecting these process representations in Arctic SVS2-Crocus will improve simulation of snow density profiles, which is crucial for many applications.

Estimates of winter (snow-covered non-growing season) CO2 fluxes across the Arctic region vary by a factor of 3.5, with considerable variation between measured and simulated fluxes. Measurements of snow properties, soil temperatures, and net ecosystem exchange (NEE) at Trail Valley Creek, NWT, Canada, allowed for the evaluation of simulated winter NEE in a tundra environment with the Community Land Model (CLM5.0). Default CLM5.0 parameterisations did not adequately simulate winter NEE in this tundra environment, with near-zero NEE (< 0.01 gCm-2d-1) simulated between November and mid-May. In contrast, measured NEE was broadly positive (indicating net CO2 release) from snow-cover onset until late April. Changes to the parameterisation of snow thermal conductivity, required to correct for a cold soil temperature bias, reduced the duration for which no NEE was simulated. Parameter sensitivity analysis revealed the critical role of the minimum soil moisture threshold of decomposition (Ψmin) in regulating winter soil respiration. The default value of this parameter (Ψmin) was too high, preventing simulation of soil respiration for the vast majority of the snow-covered season. In addition, the default rate of change of soil respiration with temperature (Q10) was too low, further contributing to poor model performance during winter. As Ψmin and Q10 had opposing effects on the magnitude of simulated winter soil respiration, larger negative values of Ψmin and larger positive values of Q10 are required to simulate wintertime NEE more adequately.

Abstract. The precise magnitude and timing of permafrost-thaw-related emissions and their subsequent impact on the global climate system remain highly uncertain. This uncertainty stems from the complex quantification of the rate and extent of permafrost thaw, which is influenced by factors such as sensitivity to surface properties like snow cover. Acting as a thermal insulator, snow cover directly influences surface energy fluxes and can significantly impact the permafrost thermal regime. However, current Earth System Models often inadequately represent the nuanced effects of snow cover in permafrost regions, leading to inaccuracies in simulating soil temperatures and permafrost dynamics. Notably, CLM5.0 tends to overestimate snowpack thermal conductivity over permafrost regions, resulting in an underestimation of the snow insulating capacity. By using a snow thermal conductivity scheme better adapted for snowpack typically found in permafrost regions, we seek to resolve thermal insulation underestimation and assess the influence of snow on simulated soil temperatures and permafrost dynamics. Evaluation using two Arctic-wide soil temperature observation datasets reveals that the new snow thermal conductivity scheme noticeably reduces the cold soil temperature bias (RMSE = 3.17 to 2.4 °C, using remote sensing data; RMSE = 3.9 to 2.19 °C, using in-situ data) and effectively addresses the overestimation of permafrost extent present when using the default parameterizations of CLM5.0.

Accurate simulations of snow emission in surface-sensitive microwave channels are needed to separate snow from atmospheric information essential for numerical weather prediction. Measurements from a field campaign in Trail Valley Creek, Inuvik, Canada, during March 2018 were used to evaluate the Snow Microwave Radiative Transfer (SMRT) model at 89 GHz and, for the first time, frequencies between 118 and 243 GHz. In situ data from 29 snow pits, including snow specific surface area, were used to calculate exponential correlation lengths to represent the snow microstructure and to initialize snowpacks for simulation with SMRT. Measured variability in snowpack properties was used to estimate uncertainty in the simulations. SMRT was coupled with the Atmospheric Radiative Transfer Simulator to account for the directionally dependent emission and attenuation of radiation by the atmosphere. This is a major developmental step needed for top-of-atmosphere simulations of microwave brightness temperature at atmosphere-sensitive frequencies with SMRT. Nadir-simulated brightness temperatures at 89, 118, 157, 183 and 243 GHz were compared with airborne measurements and with ground-based measurements at 89 GHz. Inclusion of anisotropic atmospheric radiance in SMRT had the greatest impact on brightness temperature simulations at 183 GHz and the least impact at 89 GHz. Medians of simulations compared well with medians of observations, with a root mean squared difference of 14 K across five frequencies and two flights (n=10). However, snow pit measurements did not capture the observed variability fully as simulations and airborne observations formed statistically different distributions. Topographical differences in simulated brightness temperature between sloped, valley and plateau areas diminished with increasing frequency as the penetration depth within the snow decreased and less emission from the underlying ground contributed to the airborne observations. Observed brightness temperature differences between flights were attributed to the deposition of a thin layer of very-low-density snow. This illustrates the need to account for both temporal and spatial variabilities in surface snow microstructure at these frequencies. Sensitivity to snow properties and the ability to reflect changes in observed brightness temperature across the frequency range for different landscapes, as demonstrated by SMRT, are necessary conditions for inclusion of atmospheric measurements at surface-sensitive frequencies in numerical weather prediction.

Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Recently, snow routines in hydrological and land surface models were improved to incorporate more accurate representations of forest snow processes, but model intercomparison projects have identified deficiencies, partly due to incomplete knowledge of the processes controlling snow cover in forests. The Snow Under Forest (SnoUF) project was initiated to enhance knowledge of the complex interactions between snow and vegetation. Two field campaigns, during the winters 2016–2017 and 2017–2018, were conducted in a coniferous forest bordering the snow study at Col de Porte (1325 m a.s.l., French Alps) to document the snow accumulation and ablation processes. This paper presents the field site, the instrumentation and the collection and postprocessing methods. The observations include distributed forest characteristics (tree inventory, lidar measurements of forest structure, subcanopy hemispherical photographs), meteorology (automatic weather station and an array of radiometers), snow cover and depth (snow pole transect and laser scan) and snow interception by the canopy during precipitation events. The weather station installed under dense canopy during the first campaign has been maintained since then and has provided continuous measurements throughout the year since 2018. Data are publicly available from the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data center at https://doi.org/10.17178/SNOUF.2022 (Sicart et al., 2022).

Around 22% of the global population depend on mountain runoff for their water supply. Due to its importance for future water resources, as well as flood and drought planning, an improved understanding of spatial precipitation patterns in mountain regions is needed. Precipitation gauge networks are sparse and traditional methods of interpolation yield inadequate precipitation fields for poorly gauged mountain catchments.This research project builds on a new method, Random Mixing, to generate multiple random spatial daily precipitation fields, conditioned on gauge observations. The Random Mixing algorithm has so far been tested on larger, densely gauged catchments. This project adapts the approach for a sparsely gauged, small 9.1 km2 mountain catchment, Marmot Creek Research Basin in Alberta, Canada, where elevations range between 1600 m and 2825 m above sea level (a.s.l.). Quality-controlled total precipitation (i.e., rainfall and snowfall) gauge observations, for an 11-year period, from three weather stations around the catchment have been used to condition the random spatial fields.To optimise selection of the most plausible fields, ensemble hydrological simulations are run, initially using a Python-coded version of the HBV spatially-distributed conceptual model, on a 50 m2 regular model grid. Optimisation involves the use of metrics, primarily Nash-Sutcliffe Efficiency (NSE) and bias, to identify the fields that result in the best match between observed and simulated streamflows. Sensitivity of these fields to seasonality, elevation and precipitation intensity is tested.Results so far are promising. Even with very few gauges, improving the way that spatial covariance relationships between gauge locations are represented in the model has enhanced the quality of the spatial fields. The biggest improvement to date is from explicitly modelling the precipitation / elevation relationship, introducing gradients, and applying daily dry day and wet day parameters to each grid cell across the model domain.Intended future work will aim to further refine the process using a physically-based spatially distributed model, the Cold Regions Hydrological Model (CRHM). Spatial fields generated using other random methods will be used to evaluate the performance of the new technique. Long time-period flood frequency curves generated using each approach will be compared. Different methods of phase partitioning will be evaluated to identify impacts on extreme flooding which is often controlled by snowpack melt. Climate change perturbations will be applied to generate potential future flood estimates.

Abstract. The carbon cycle in Arctic-boreal regions (ABR) is an important component of the planetary carbon balance, with growing concerns about the consequences of ABR warming on the global climate system. The greatest uncertainty in annual carbon dioxide (CO2) budgets exists during the non-growing season, primarily due to challenges with data availability and limited spatial coverage in measurements. The goal of this study was to determine the main environmental controls of non-growing season CO2 fluxes in ABR over a latitudinal gradient (45° N to 69° N) featuring four different ecosystem types: closed-crown coniferous boreal forest, open-crown coniferous boreal forest, erect-shrub tundra, and prostrate-shrub tundra. CO2 fluxes calculated using a snowpack diffusion gradient method (n = 560) ranged from 0 to 1.05 gC m2 day-1. To assess the dominant environmental controls governing CO2 fluxes, a Random Forest machine learning approach was used. We identified that soil temperature as the main control of non-growing season CO2 fluxes with 68 % of relative model importance, except when soil liquid water occurred during zero degree Celsius curtain conditions (Tsoil ≈ 0 °C and liquid water coexists with ice in soil pores). Under zero-curtain conditions, liquid water content became the main control of CO2 fluxes with 87 % of relative model importance. We observed exponential regressions between CO2 fluxes and soil temperature (RMSE = 0.024 gC m-2 day-1) in frozen soils, as well as liquid water content (RMSE = 0.137 gC m-2 day-1) in zero-curtain conditions. This study is showing the role of several variables on the spatio-temporal variability of CO2 fluxes in ABR during the non-growing season and highlight that the complex vegetation-snow-soil interactions in northern environments must be considered when studying what drives the spatial variability of soil carbon emission during the non-growing season.