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Arctic tundra ecosystems have experienced unprecedented change associated with climate warming over recent decades. Across the Pan-Arctic, vegetation productivity and surface greenness have trended positively over the period of satellite observation. However, since 2011 these trends have slowed considerably, showing signs of browning in many regions. It is unclear what factors are driving this change and which regions/landforms will be most sensitive to future browning. Here we provide evidence linking decadal patterns in arctic greening and browning with regional climate change and local permafrost-driven landscape heterogeneity. We analyzed the spatial variability of decadal-scale trends in surface greenness across the Arctic Coastal Plain of northern Alaska (similar to 60,000 km(2)) using the Landsat archive (1999-2014), in combination with novel 30 m classifications of polygonal tundra and regional watersheds, finding landscape heterogeneity and regional climate change to be the most important factors controlling historical greenness trends. Browning was linked to increased temperature and precipitation, with the exception of young landforms (developed following lake drainage), which will likely continue to green. Spatiotemporal model forecasting suggests carbon uptake potential to be reduced in response to warmer and/or wetter climatic conditions, potentially increasing the net loss of carbon to the atmosphere, at a greater degree than previously expected.
The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.
The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.
Degrading permafrost can alter ecosystems, damage infrastructure, and release enough carbon dioxide (CO2) and methane (CH4) to influence global climate. The permafrost carbon feedback (PCF) is the amplification of surface warming due to CO2 and CH4 emissions from thawing permafrost. An analysis of available estimates PCF strength and timing indicate 120 +/- 85 Gt of carbon emissions from thawing permafrost by 2100. This is equivalent to 5.7 +/- 4.0% of total anthropogenic emissions for the Intergovernmental Panel on Climate Change (IPCC) representative concentration pathway (RCP) 8.5 scenario and would increase global temperatures by 0.29 +/- 0.21 degrees C or 7.8 +/- 5.7%. For RCP4.5, the scenario closest to the 2 degrees C warming target for the climate change treaty, the range of cumulative emissions in 2100 from thawing permafrost decreases to between 27 and 100 Gt C with temperature increases between 0.05 and 0.15 degrees C, but the relative fraction of permafrost to total emissions increases to between 3% and 11%. Any substantial warming results in a committed, long-term carbon release from thawing permafrost with 60% of emissions occurring after 2100, indicating that not accounting for permafrost emissions risks overshooting the 2 degrees C warming target. Climate projections in the IPCC Fifth Assessment Report (AR5), and any emissions targets based on those projections, do not adequately account for emissions from thawing permafrost and the effects of the PCF on global climate. We recommend the IPCC commission a special assessment focusing on the PCF and its impact on global climate to supplement the AR5 in support of treaty negotiation.
Ice-wedge polygons are common features of northeastern Siberian lowland periglacial tundra landscapes. To deduce the formation and alternation of ice-wedge polygons in the Kolyma Delta and in the Indigirka Lowland, we studied shallow cores, up to 1.3 m deep, from polygon center and rim locations. The formation of well-developed low-center polygons with elevated rims and wet centers is shown by the beginning of peat accumulation, increased organic matter contents, and changes in vegetation cover from Poaceae-, Alnus-, and Betula-dominated pollen spectra to dominating Cyperaceae and Botryoccocus presence, and Carex and Drepanocladus revolvens macro-fossils. Tecamoebae data support such a change from wetland to open-water conditions in polygon centers by changes from dominating eurybiontic and sphagnobiontic to hydrobiontic species assemblages. The peat accumulation indicates low-center polygon formation and started between 2380 +/- 30 and 1676 +/- 32 years before present (BP) in the Kolyma Delta. We recorded an opposite change from open-water to wetland conditions because of rim degradation and consecutive high-center polygon formation in the Indigirka Lowland between 2144 +/- 33 and 1632 +/- 32 years BP. The late Holocene records of polygon landscape development reveal changes in local hydrology and soil moisture.
Sub-seasonal thaw slump mass wasting is not consistently energy limited at the landscape scale
(2018)
Predicting future thaw slump activity requires a sound understanding of the atmospheric drivers and geomorphic controls on mass wasting across a range of timescales. On sub-seasonal timescales, sparse measurements indicate that mass wasting at active slumps is often limited by the energy available for melting ground ice, but other factors such as rainfall or the formation of an insulating veneer may also be relevant. To study the sub-seasonal drivers, we derive topographic changes from single-pass radar interferometric data acquired by the TanDEM-X satellites. The estimated elevation changes at 12m resolution complement the commonly observed planimetric retreat rates by providing information on volume losses. Their high vertical precision (around 30 cm), frequent observations (11 days) and large coverage (5000 km(2)) allow us to track mass wasting as drivers such as the available energy change during the summer of 2015 in two study regions. We find that thaw slumps in the Tuktoyaktuk coastlands, Canada, are not energy limited in June, as they undergo limited mass wasting (height loss of around 0 cm day 1) despite the ample available energy, suggesting the widespread presence of early season insulating snow or debris veneer. Later in summer, height losses generally increase (around 3 cm day 1), but they do so in distinct ways. For many slumps, mass wasting tracks the available energy, a temporal pattern that is also observed at coastal yedoma cliffs on the Bykovsky Peninsula, Russia. However, the other two common temporal trajectories are asynchronous with the available energy, as they track strong precipitation events or show a sudden speed-up in late August respectively. The observed temporal patterns are poorly related to slump characteristics like the headwall height. The contrasting temporal behaviour of nearby thaw slumps highlights the importance of complex local and temporally varying controls on mass wasting.