ALBERT

Description: During the last decade the Arctic has experienced increasing human development while many native communities continue to live a subsistence lifestyle. Off-road winter tundra travel for resource exploration is most cost effective and least environmentally damaging during winter when the tundra is frozen and snow covered. Climate warming, which is occurring at an amplified rate in the Arctic, likely changes the period when access to the off-road tundra travel is possible. There currently exists, however, large uncertainty as to how climate change will impact the low-cost winter travel access across the tundra. Here we defined safe tundra access when soil temperatures are below a soil type dependent freezing temperature and snow cover is at least 20 cm. Our analysis is based on the simulated soil temperatures and snow depths of Land Surface Models (LSMs) contributing to “The Inter-Sectoral Impact Model Intercomparison Project” (ISIMIP). ISIMIP simulations are based on a common protocol, the same input data, the same spatial (0.5°) and temporal resolution (daily modeling output), and span over the period 1861-2100. The LSMs are forced by four different bias-corrected global circulation models (IPSL-CM5A-LR, GFDL-ESM2M, MIROC5, HadGEM2-ES) and three different future conditions (represented via representative concentration pathways (RCP) 2.6, 6.0, 8.5). The simulation results of our model ensemble (60 model combinations) show consistent permafrost warming and changing snow cover patterns at 60°N. Annual off-road tundra travel is considerably reduced (〉50%) under future climate change scenarios, especially under the RCP8.5. The main reduction can be observed in the spring and autumn (〉30%). The results of the multi-model ensemble differ in magnitude, however, their overall trend is consistent. Our results suggest a high vulnerability and substantial changes to the (subsistence) livelihoods of native communities and increasing costs for off-road resource exploration.

Description: The ESA DUE Permafrost project (2009-2011) is developing a suite of parameters indicative of the subsurface phenomenon permafrost using satellite remote sensing: Land Surface Temperature (LST), Surface Soil Moisture (SSM), Surface Frozen and Thawed State (Freeze/Thaw), Terrain, Land Cover (LC), and Surface Water (SW). Snow parameters (Snow Extent and Snow Water Equivalent) are being developed through the DUE GlobSnow project, Global Snow Monitoring for Climate Research (2008-2011). The final DUE Permafrost remote sensing products cover the years 2007 to 2011 with a circumpolar coverage that will soon be released (early 2012), and then be used to analyze the temporal dynamics and map the spatial patterns of indicators. Further information is available at www.ipf.tuwien.ac.at/ permafrost. Since the beginning, scientific stakeholders and the International Permafrost Association (IPA) have been involved in the science and implementation plan. Interactive international user workshops took place in 2010 at the Technical University of Vienna, Vienna (AT), and in 2011 at the International Arctic Research Center (IARC), Fairbanks, Alaska (US). This involvement and the ongoing evaluation of the indicators derived from remote sensing for the high-latitude permafrost regions make the DUE Permafrost products trustworthy for the permafrost and the climate research community. The adaption of the remote sensing products for the permafrost and climate modelling is experimental and highly dependent on the users’ involvement. For a few years already, the Geophysical Institute Permafrost Laboratory (GIPL), University of Alaska Fairbanks, US, (http://www.gi.alaska.edu/research/snowicepermafrost/Permafrost) has successfully demonstrated the value of using LST derived from remote sensing data for driving its permafrost models. Further experimental testing of the DUE Permafrost products for use by the modeling community (permafrost and climate) will range from (i) the evaluation of external data of the models, with modifying or providing new external data (e.g. tundra land cover, surface water ratio, soil distribution), to (ii) new drivers for regional models derived from remote sensing (e.g., LST), to (iii) the evaluation of the output data from the models (e.g. spatial patterns of moisture and temperature).

Description: The focus of this research has been on detecting changes in lakes vegetation, land surface temperatures, and snow cover, using data from remote sensing. The study area covers the main (central) part of the Lena River catchment in the Yakutia Region of Siberia (Russia) where continuous permafrost coverage’s is up to 90%. The remote sensing analyses are based on MODIS (NASA) and Landsat (USGS) satellite data. Time series of remote sensing products of MODIS land surface temperature were produced for the study region between 61°N and 65°N, and between 117.5°E and 131.5°E. The MODIS Land Surface Temperature level 3 product, MOD11C3 are configured on a 0.05° latitude/longitude MODIS Climate Model Grid (CMG) raster. The LST product is a monthly composited average and represents clear-sky LST values. The monthly land surface temperature were analyzed over the eleven year interval from May 2000 to April 2011. Linear trend calculations for the 11 year temperature measurement interval were performed separately for each two month interval, in each pixel, using the least squares method. Water bodies were extracted using the Landsat Short Wave Infrared SWIR band 5. Within the study region's 315,000 sq. km, the total area covered by lakes increased by 17.5% between 2002 and 2009. The amount of lake increase differs between 42-11% depending on the region. The overall trend in land surface temperature is around 0.15°C/year, but with seasonal warming trends in April-May of up to 0.45°C/year in some areas and cooling of -0.2 to -0.3°C/year in July-August in other areas. These regional differences and potential causes of the land surface temperature changes will be discussed with respect to land cover changes.

Description: The Lena River Delta in Northern Yakutia forms one of the largest deltas in the Arctic and its catchment area (2 430 000 km2) is one of the largest in the whole of Eurasia. The Lena River distributes water and sediment in four main channels before discharging in total about 30 km3 of water through the delta into the Arctic Ocean every year and its discharge has been observed to be increasing. The goal of this presentation is to characterize the hydrologic processes that are strongly affected by a transient climate component- the permafrost. Permafrost plays a major role for storage and release of water to rivers and surface and subsurface water bodies. Conversely, the coupled water and heat fluxes in the atmosphere and below ground have a marked influence on the permafrost’s thermal regime. Our study site, the Lena River Delta, is also one of the coldest and driest places on Earth, with mean annual air temperatures of about -13 °C, a large annual air temperature range of about 44 °C and summer precipitation usually less than 150 mm. Very cold continuous permafrost of about −8.6 °C (11 m depth) underlays the area between about 400 and 600 m below surface and since 2006 the permafrost has warmed than 1 °C at 10.7 m. Roughly half of the land surface is dominated by wet surfaces, such as polygons, ponds and thermokarst lakes. This contribution summarizes past and ongoing research on hydrologic processes across spatial scales, from microtopographic processes of polygonal tundra to regional scale deltaic processes to assess short and long term changes in water fluxes. We quantify unfrozen water in soils, streams and river discharges and water bodies’ storage at larger scales. Water bodies were mapped using optical and radar satellite data with resolutions of 4 m or better, Landsat-5 TM at 30 m and the MODIS water mask at 250 m resolution. Ponds, i. e. water bodies with surface are smaller than 104 m, make over 95 % of the total number of water bodies and are not resolved in Landsat-scale land cover classifications. Ponds are generally well mixed and experience high water temperatures up to 23 °C during the summer and are, therefore, hotspots for biological activity and CO2 emission. The ponds in the study area freeze completely in winter, whereas the deeper thermokarst lakes do not freeze to the bottom, with implications for coupling of the permafrost to the atmosphere. These deep thermokarst lakes are thermally stratified during winter and reach maximum water temperatures of up to 19 °C during summer. The summer water balance at the catchment scale was found to be mainly controlled by vertical fluxes (precipitation and evapotranspiration). On the other hand, redistribution of storage water due to lateral fluxes takes place within the microtopography of polygonal tundra. The long-term summer storage (precipitation minus evapotranspiration) from 1958-2011 indicates a reasonably balance on the polygonal tundra with an average surplus of 5 mm, but it is also characterized by high interannual variability due to precipitation input. During negative water balance years where evapotranspiration exceeds precipitation, shallower water bodies dry out. The extent of wetlands and water bodies will shift with changes in vertical water fluxes as well as permafrost warming and thaw. Thus, water bodies can serve as sentinels of environmental change and we present applicable remote-sensing observations and upscaling methods

Description: Deciduous larch is a weak competitor when growing in mixed stands with evergreen taxa but is dominant in many boreal forest areas of Eastern Siberia. However, it is hypothesized that certain factors such as a shallow active layer thickness and high fire frequency favor larch dominance. Our aim is to understand how thermohydrological interactions between vegetation, permafrost, and atmosphere stabilize the larch forests and the underlying permafrost in Eastern Siberia. A tailored version of a one-dimensional land surface model (CryoGrid) is adapted for the application in vegetated areas and used to reproduce the energy transfer and thermal regime of permafrost ground in typical boreal larch stands. In order to simulate the responds of Arctic trees to local climate and permafrost conditions we have implemented a multilayer canopy parameterization originally developed for the Community Land Model (CLM-ml_v0). The coupled model is capable of calculating the full energy balance above, within and below the canopy including the radiation budget, the turbulent fluxes and the heat budget of the permafrost ground under several forcing scenarios. We will present first results of simulations performed for different study sites in larch-dominated forests of Eastern Siberia and Mongolia under current and future climate conditions. Model performance is thoroughly evaluated based on comprehensive in-situ soil temperature and radiation measurements at our study sites.

Description: With the Earth’s climate rapidly warming, the Arctic represents one of the most vulnerable regions to environmental change. These northern high latitude regions experience intensified fire seasons and especially tundra fires are projected to become more frequent and severe. Fires in permafrost regions have extensive impacts, including the initiation of thermokarst (rapid thaw of ice-rich ground), as they combust the upper organic soil layers which provide insulation to the permafrost below. Rapid permafrost thaw is, thus, often observable in fire scars in the first years post-disturbance. In polygonal ice-wedge landscapes, this becomes most prevalent through melting ice wedges and degrading troughs. The further these ice wedges degrade, the more troughs will likely connect and build an extensive hydrological network with changing patterns and degrees of connectivity that influences hydrology and runoff. While subsiding troughs over melting ice wedges may host new ponds, an increasing connectivity may also subsequently lead to more drainage of ponds, which in turn can limit further thaw and help stabilize the landscape. To quantify the changes in such dynamic landscapes over large regions, highly automated methods are needed that allow extracting information on the geomorphic state and changes over time of ice-wedge trough networks from remote sensing data. We developed a computer vision algorithm to automatically derive ice-wedge polygonal networks and the current microtopography of the degrading troughs from very high resolution, airborne laserscanning-based digital terrain models. We represent the networks as graphs (a concept from the computer sciences to describe complex networks) and apply methods from graph theory to describe and quantify hydrological network characteristics of the changing landscape. In fire scars, we especially observe rapidly growing networks and fast micromorphological change in those degrading troughs. In our study, we provide a space-for-time substitution comparing fire scars throughout the Alaskan tundra of up to 70 years since the fire disturbance, to show how this type of disturbed landscape evolves over time.

Description: As air temperatures rise and sea ice cover declines in the Arctic, permafrost coastal cliffs thaw more rapidly and wave energy rises. Thus, as the open water season continues to lengthen, climate change triggers a large part of the Arctic shoreline to become increasingly vulnerable to erosion. Arctic erosion supplies nutrient-laden and carbon-rich sediment into nearshore ecosystems. A retreating coastline also has consequences for residential, cultural, and industrial infrastructure. Despite its importance, erosion is currently neglected in global climate models, and existing physics-based numerical models of Arctic shoreline erosion are too complex and regionally-focused to be applied on a pan-Arctic scale. Here, we apply our simplified numerical erosion model, ArcticBeach v1.0, to the entire Arctic coastline. ArcticBeach v1.0 has previously been shown to simulate retreat rates at two sites that differ substantially in their main mechanisms of retreat (sub-aerial erosion/thaw slumping versus notch/block erosion). The model uses heat and sediment volume balances in order to predict horizontal cliff retreat and vertical erosion of a fronting beach. It contains an erosion module that uses empirical equations to estimate cross-shore sediment transport, coupled to a storm surge module forced by wind. We present Arctic maps of regional variation in trends in 2-meter air temperature, sea ice concentration, and wind speed.