ALBERT

Description: In this study we simulate the climatic mass balance of Svalbard glaciers with a coupled atmosphere-glacier model with 3 km grid spacing, from September 2003 to September 2013. We find a mean specific net mass balance of −167 mm w.e. yr−1, corresponding to a mean annual mass loss of about 5.7 Gt, with large interannual variability. Our results are compared with a comprehensive set of mass balance, meteorological and satellite measurements. Model temperature biases of 0.17 and −1.9 °C are found at two glacier automatic weather station sites. Simulated climatic mass balance is mostly within about 0.1 m w.e. yr−1 of stake measurements, and simulated winter accumulation at the Austfonna ice cap shows mean absolute errors of 0.05 and 0.06 m w.e. yr−1 when compared to radar-derived values for the selected years 2004 and 2006. Comparison of surface height changes from 2003 to 2008 from model, and satellite altimetry reveals good agreement in both mean values and regional differences. The largest deviations from observations are found for winter accumulation at Hansbreen (up to around 1 m w.e. yr−1), a site where sub-grid topography and wind redistribution of snow are important factors. Comparison with simulations using a 9 km grid spacing reveal considerable differences on regional and local scales. In addition, the 3 km grid spacing allows for a much more detailed comparison with observations than what is possible with a 9 km grid spacing. Further decreasing the grid spacing to 1 km appears to be less significant, although in general precipitation amounts increase with resolution. Altogether, the model compares well with observations and offers possibilities for studying glacier climatic mass balance on Svalbard both historically as well as based on climate projections.

Description: Peat plateaus and palsas are characteristic morphologies of sporadic permafrost, and the transition from permafrost to permafrost‐free ground typically occurs on spatial scales of meters. They are particularly vulnerable to climate change and are currently degrading in Fennoscandia. Here we present a spatially distributed data set of ground surface temperatures for two peat plateau sites in northern Norway for the year 2015–2016. Based on these data and thermal modeling, we investigate how the snow depth and water balance modulate the climate signal in the ground. We find that mean annual ground surface temperatures are centered around 2 to 2.5 °C for stable permafrost locations and 3.5 to 4.5 °C for permafrost‐free locations. The surface freezing degree days are characterized by a noticeable threshold around 200 °C.day, with most permafrost‐free locations ranging below this value and most stable permafrost ones above it. Freezing degree day values are well correlated to the March snow cover, although some variability is observed and attributed to the ground moisture level. Indeed, a zero curtain effect is observed on temperature time series for saturated soils during winter, while drained peat plateaus show early freezing surface temperatures. Complementarily, modeling experiments allow identifying a drainage effect that can modify 1‐m ground temperatures by up to 2 °C between drained and water accumulating simulations for the same snow cover. This effect can set favorable or unfavorable conditions for permafrost stability under the same climate forcing.

Description: Thawing of permafrost potentially affects the global climate system through the mobilization of greenhouse gases, and poses a risk to human infrastructure in the Arctic. The response of ice-rich permafrost landscapes to a changing climate is particularly uncertain, and challenging to be addressed with numerical models. A main reason for this is the rapidly changing surface topography resulting from melting of ground ice, which is referred to as thermokarst. It is expressed in characteristic landforms which alter the hydrology, the surface energy balance, and the redistribution of snow of the entire landscapes. Polygonal patterned tundra which is underlain by massive ice-wedges, is a prototype of a sensitive permafrost system which is increasingly subjected to thermokarst activity throughout the Arctic. In this talk I will present a scalable modeling approach, based on the CryoGrid land surface model, to investigate the degradation of ice-wedges. The numerical model takes into account lateral fluxes of heat, water, and snow between different topographic units of polygonal tundra and simulates topographic changes resulting from melting of excess ground ice (i.e., thermokarst), and from lateral erosion of sediment. We applied the model to investigate the influence of hydrological conditions on the development of different types of ice-wedge polygons in a study area in northern Siberia. We further used projections of future climatic conditions to confine the evolution of ice-wedge polygons in a changing climate, and assessed the amount of organic matter which could thaw under different scenarios. In a related study for a study site in northern Alaska, we demonstrated that the model setup can be used to study the effect of infrastructure on the degradation of ice-wedges. Altogether, our modeling approach can be seen as a blueprint to investigate complexly inter-related processes in ice-rich permafrost landscapes, and marks a step forward towards an improved representation of these landscapes in large-scale land surface models.

Description: conductivity, is a key control on the thermal state of near surface permafrost. At the same time, accurately estimating the seasonal snow cycle at the kilometre scale is a considerable hydrometeorological challenge. Consequently, snow represents a major source of uncertainty in permafrost models. To constrain this snow induced uncertainty we propose a new ensemblebased snow data assimilation framework (ESDA) for fine scale snow state estimation that fuses a simple subgrid snow model and fine scale satellite-based surface albedo retrievals using the ensemble Kalman filter (EnKF; reviewed in Evensen [2009]). The potential of ESDA is demonstrated for the Bayelva catchment near Ny Åelsund (Svalbard, Norway) where independent ground-based observations of snow cover and the near surface ground thermal state were available to perform validation. On the modeling side of ESDA we adopt the subgrid snow distribution model (SSNOWD; see Liston [2004]) to estimate the snow water equivalent depth distribution, snow cover fraction and surface albedo at the grid scale (1 km). These model runs are forced by melt and net precipitation rates based on the energy and water balance derived from the meteorological fields provided by a (3 km resolution) Weather Research and Forecasting (WRF) model run. For observations our system makes combined use of two relatively new high level products: frequently available coarser scale (500 m) albedo retrievals from MODIS (MCD43A version 6) and intermittently available finer scale (30 m) albedos derived from Landsat8 surface reflectance retrievals. In the last step of the framework we apply the EnKF; a robust sequential data assimilation method that yields the optimal estimate of a system state based on the combined information from model results and observations, both of which are uncertain, provided a set of assumptions hold (see Evensen [2009]). The EnKF has been successfully implemented for a range of applications in numerous fields including oceanography, meteorology, hydrology, mining and reservoir geophysics, although to our knowledge this is the first time it is being applied directly to permafrost modeling. Simply stated an ensemble (a set) of model realizations, in this case capturing uncertainties in the meteorological forcing, are propagated forward in time and sequentially updated by the observations whenever these are available. The magnitude of the updates depends on the deviation of the model realizations from the observations as well as the respective uncertainties. Thereby, the result of the EnKF is expressed in terms of an ensemble of corrected model states, where the ensemble mean is interpreted as the most likely estimate and the ensemble spread is a measure of the uncertainty. Our results are promising; the evolution of the ensemble mean estimated snow cover using ESDA at Bayelva is shown to be much closer to the ground-truth, as observed by an independent automatic camera system, than that of the open-loop (no assimilation) estimate. Finally, we incorporate ESDA into the recently developed CryoGrid3 surface energy-balance driven permafrost model described in Westermann et al. [2016]. The results, with and without ESDA, are compared to in situ measurements from an array of randomly distributed ground surface temperature measurements within the modeled grid cell. A significant improvement in the skill of the model at capturing the near-surface ground thermal state is demonstrated, particularly in the ablation season. Thus, ESDA provides improved estimates of the state of permafrost at Bayelva. Due to the cheap computational cost, the framework is also applicable to much larger model domains. Moreover, given the robustness, owing to the global span of the satellite retrievals and the option of running SSNOWD with reanalysis data (e.g. ERA-Interim), it is possible to apply this framework to most permafrost regions on the planet.