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  • 1
    Publication Date: 2014-12-13
    Description: The annual variability of CO2 exchange in most ecosystems is primarily driven by the activities of plants and soil microorganisms. However, little is known about the carbon balance and its controlling factors outside the growing season in Arctic regions dominated by soil freeze/thaw processes, long-lasting snow cover, and several months of darkness. This study presents a complete annual cycle of the CO2 net ecosystem exchange (NEE) dynamics for a high Arctic tundra area at the west coast of Svalbard based on eddy covariance flux measurements. The annual cumulative CO2 budget is close to 0 g C m−2 yr−1, but displays a strong seasonal variability. Four major CO2 exchange seasons have been identified. (1) During summer (snow-free ground), the CO2 exchange occurs mainly as a result of biological activity, with a dominance of strong CO2 assimilation by the ecosystem. (2) The autumn (snow-free ground or partly snow-covered) is dominated by CO2 respiration as a result of biological activity. (3) In winter and spring (snow-covered ground), low but persistent CO2 release occurs, overlayed by considerable CO2 exchange events in both directions associated with high wind speed and changes of air masses and atmospheric air pressure. (4) The snow melt season (pattern of snow-free and snow-covered areas) is associated with both meteorological and biological forcing, resulting in a carbon uptake by the high Arctic ecosystem. Data related to this article are archived at http://doi.pangaea.de/10.1594/PANGAEA.809507.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
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  • 2
    Publication Date: 2014-12-11
    Description: In permafrost environments exposed to strong winds, drifting snow can create a small-scale pattern of strongly variable snow heights, which has profound implications for the thermal regime of the ground. Arrays of 26 to more than 100 temperature loggers were installed to record the distribution of ground surface temperatures within three study areas across a climatic gradient from continuous to sporadic permafrost in Norway. A variability of the mean annual ground surface temperature of up to 6°C was documented within areas of 0.5 km2. The observed variation can, to a large degree, be explained by variation in snow height. Permafrost models, employing averages of snow height for grid cells of, e.g., 1 km2, are not capable of representing such sub-grid variability. We propose a statistical representation of the sub-grid variability of ground surface temperatures and demonstrate that a simple equilibrium permafrost model can reproduce the temperature distribution within a grid cell based on the distribution of snow heights.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
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  • 3
    Publication Date: 2015-03-20
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed , info:eu-repo/semantics/article
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  • 4
    Publication Date: 2017-01-21
    Description: With current remote sensing technologies, it is not possible to directly infer the thermal state of the ground from spaceborne platforms. We demonstrate that such limitations can be overcome by combining the information content of several remote sensing products in a data fusion approach: time series of remotely sensed land surface temperature, as well as snow cover and snow water equivalent, are employed to force ground thermal models which deliver ground temperatures and thaw depths. First, we present a semi-empirical model approach based on remotely sensed land surface temperatures and reanalysis products from which mean annual ground temperatures (MAGT) can be estimated at a spatial resolution of 1 km at continental scales. The approach is tested for the unglacierized land areas in the North Atlantic region, an area of more than 5 million km2. The results are compared to in-situ temperature measurements in more than 100 boreholes from which the accuracy of the scheme is estimated to approximately 2.5 °C. Furthermore, we explore transient modeling of ground temperatures driven by remotely sensed land surface temperature, snow cover and snow water equivalent. The permafrost model CryoGrid 2 is applied to the Lena River Delta in NE Siberia ( 25000 km2) at 1km spatial and weekly time resolution for the period 2000–2014. A comparison to in-situ measurements suggests a possible accuracy of around 1 °C for annual average ground temperatures, and around 0.1 m for thaw depth. However, information on subsurface stratigraphies including the distribution of ground ice is required to achieve this accuracy which is currently not available from remote sensing products alone. Finally, we discuss the potential and limitations of schemes using a combination of remote sensing data and thermal permafrost models to assess the thermal state of the ground, and give a feasibility assessment for both mountain and lowland permafrost regions.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , NonPeerReviewed
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  • 5
    Publication Date: 2017-01-21
    Description: Lakes and ponds are abundant in vast regions of the permafrost lowland landscapes in the Arctic. The areal fraction of open water surfaces can amount to more than 25 % in some lowland tundra landscapes. In some tundra landscapes, about 50 % of the total number of water bodies feature surface areas less than 10 m2. Several studies emphasize that these water bodies strongly control fundamental ecosystem processes such as the carbon, heat, and water balance. So far, it is poorly understood how these ecosystems will respond to changing climate conditions. In particular, the presence of water bodies is strongly related to the stability of the surrounding permafrost soils. Permafrost is an effective water barrier that largely controls lake formation, drainage, and growth. In return, water bodies strongly affect the thermal state of the surrounding permafrost by modifying the surface energy balance and the subsurface heat transport and storage capabilities. In order to gain a better understanding of the vulnerability of such landscapes the 1D transient permafrost model CryoGrid3 was coupled to the 1D lake model FLake. The development of the model was supported by a large observational dataset of water temperature profile measurements from lakes and ponds in northern Siberia. The coupled model was used for site level simulations for water bodies on Samoylov Island located in the Lena River Delta. Based on extensive Monte-Carlo sensitivity tests, we investigated the thermal impact of water bodies with different depths (0.2 – 5.0 m) on the thermal state of sediments underneath. Climate impact simulations until 2100 were performed considering a moderate and a strong climate warming scenario. The preliminary results suggest that shallow water bodies (water depth 〈 1.5 m) can accelerate permafrost thaw by a factor of five. More importantly, the difference in permafrost thaw rate between moderate and strong climate warming vanish for water bodies deeper than 0.8 m. Furthermore, the results demonstrate that lateral heat fluxes play an important role for stabilizing permafrost underneath small water bodies.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , NonPeerReviewed
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  • 6
    Publication Date: 2017-01-21
    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.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , NonPeerReviewed
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  • 7
    Publication Date: 2017-01-21
    Description: Thawing of permafrost is governed by a complex interplay of different processes, of which only conductive heat transfer is taken into account in most model studies. However, heat conduction alone can not account for the dynamical evolution of many permafrost landscapes, e.g. in areas rich in ground ice shaped by thermokarst ponds and lakes. Novel process parameterizations are required to include such phenomena in future projections of permafrost thaw and hereby triggered climatic feedbacks. Recently, we have demonstrated a physically-based parameterization for thaw process in ice-rich ground in the per-mafrost model CryoGrid 3, which can reproduce the formation of thermokarst ponds and subsidence of the ground following thawing of ice-rich subsurface layers. Long-term simulations for different subsurface stratigraphies in the Lena River Delta, Siberia, demonstrate that the hydrological regime can both accelerate and delay permafrost thawing. If meltwater from thawed ice-rich layers can drain, the ground subsides while at the same time the for-mation of a talik is delayed. If the meltwater pools at the surface, a pond is formed which enhances heat transfer in the ground and leads to the formation of a talik. The PERMANOR project funded by the Norwegian Research Council until 2019 will extend this work by inte- grating such small-scale processes in larger-scale Earth System Models (ESMs). For this purpose, the project will explore and develop statistical approaches, in particular tiling, to represent permafrost landscape dynamics on sub-grid scale. Ultimately, PERMANOR will conceptualize process understanding from in-situ studies to develop new model algorithms and pursue their implementation in a coupled ESM framework
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , NonPeerReviewed
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  • 8
    Publication Date: 2018-07-05
    Description: Polygonal tundra and ice-rich permafrost landscapes are widespread across the Arctic and prone to rapid transitions due to ground subsidence, thermokarst or thermoerosion. Such small-scale processes are typically not resolved in large-scale Earth System models. Our goal with this study was to develop a scalable modeling approach that is capable of simulating the degradation of ice-wedges and the corresponding geomorphological transition from low- to high-centered polygons. For this, we employed the land surface model CryoGrid3 and advanced it by a hydrological infiltration scheme. We simulated the lateral exchange of heat, water, and snow between polygon centers, rims and troughs by coupling multiple realizations. We proved our modeling approach to be capable of describing the evolution of ice-wedge polygons and analyzed the hydrological and climatic conditions, which are favorable for their degradation. With this, we contributed to the understanding of landscape transitions in ice-rich permafrost regions and their representation in large-scale models.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , NonPeerReviewed
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  • 9
    Publication Date: 2019-04-03
    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.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
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  • 10
    Publication Date: 2017-01-17
    Description: Samoylov Island is centrally located within the Lena River Delta at 72° N, 126° E and lies within the Siberian zone of continuous permafrost. The landscape on Samoylov Island consists mainly of late Holocene river terraces with polygonal tundra, ponds and lakes, and an active floodplain. The island has been the focus of numerous multidisciplinary studies since 1993, which have focused on climate, land cover, ecology, hydrology, permafrost and limnology. This paper aims to provide a framework for future studies by describing the characteristics of the island's meteorological parameters (temperature, radiation and snow cover), soil temperature, and soil moisture. The land surface characteristics have been described using high resolution aerial images in combination with data from ground-based observations. Of note is that deeper permafrost temperatures have increased between 0.3 to 1.3 °C over the last five years. However, no clear warming of air and active layer temperatures is detected since 1998, though winter air temperatures during recent years have not been as cold as in earlier years.
    Type: Article , PeerReviewed
    Format: text
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