Remotely sensing potential climate change tipping points across scales.

Potential climate tipping points pose a growing risk for societies, and policy is calling for improved anticipation of them. Satellite remote sensing can play a unique role in identifying and anticipating tipping phenomena across scales. Where satellite records are too short for temporal early warning of tipping points, complementary spatial indicators can leverage the exceptional spatial-temporal coverage of remotely sensed data to detect changing resilience of vulnerable systems. Combining Earth observation with Earth system models can improve process-based understanding of tipping points, their interactions, and potential tipping cascades. Such fine-resolution sensing can support climate tipping point risk management across scales.

The role of lake size and local phenomena for monitoring ground-fast lake ice.

In this study, we assess the effect of the lake size on the accuracy of a threshold-based classification of ground-fast and floating lake ice from Sentinel-1 Synthetic Aperture Radar (SAR) imagery. For that purpose, two new methods (flood-fill and watershed method) are introduced and the results between the three classification approaches are compared regarding different lake size classes for a study area covering most of the Yamal Peninsula in Western Siberia. The focus is on April, the stage of maximum lake ice thickness, for the years 2016 and 2017. The results indicate that the largest lakes are likely most prone to errors by the threshold classification. The newly introduced methods seem to improve classification results. The results also show differences in fractions of ground-fast lake ice between 2016 and 2017, which might reflect differences in temperatures between the winters with severe impact on wildlife and freshwater fish resources in the region. Patterns of low backscatter responsible for the classification errors in the centre of the lakes were investigated and compared to the optical Sentinel-2 imagery of late-winter. Strong similarities between some patterns in the optical and SAR data were identified. They might be zones of thin ice, but further research is required for clarification of this phenomenon and its causes.

Evaluation of a MetOp ASCAT-Derived Surface Soil Moisture Product in Tundra Environments.

Satellite-derived surface soil moisture data are available for the Arctic, but detailed validation is still lacking. Previous studies have shown low correlations between in situ and modeled data. It is hypothesized that soil temperature variations after soil thaw impact MetOp ASCAT satellite-derived surface soil moisture (SSM) measurements in wet tundra environments, as C band backscatter is sensitive to changes in dielectric properties. We compare in situ measurements of water content within the active layer at four sites across the Arctic in Alaska (Barrow, Sagwon, Toolik) and Siberia (Tiksi), taken in the spring after thawing and in autumn prior to freezing. In addition to the long-term measurement fields, where sensors are installed deeper in the ground, we designed a monitoring setup for measuring moisture very close to the surface in the Lena River Delta, Siberia. The volumetric water content (VWC) and soil temperature sensors were placed in the moss organic layer in order to account for the limited penetration depth of the radar signal. ASCAT SSM variations are generally very small, in line with the low variability of in situ VWC. Short-term changes after complete thawing of the upper organic layer, however, seem to be mostly influenced by soil temperature. Correlations between SSM and in situ VWC are generally very low, or even negative. Mean standard deviation matching results in a comparably high root-mean-square error (on average 11%) for predictions of VWC. Further investigations and measurement networks are needed to clarify factors causing temporal variation of C band backscatter in tundra regions.

Sea ice, rain-on-snow and tundra reindeer nomadism in Arctic Russia.

Sea ice loss is accelerating in the Barents and Kara Seas (BKS). Assessing potential linkages between sea ice retreat/thinning and the region's ancient and unique social-ecological systems is a pressing task. Tundra nomadism remains a vitally important livelihood for indigenous Nenets and their large reindeer herds. Warming summer air temperatures have been linked to more frequent and sustained summer high-pressure systems over West Siberia, Russia, but not to sea ice retreat. At the same time, autumn/winter rain-on-snow (ROS) events have become more frequent and intense. Here, we review evidence for autumn atmospheric warming and precipitation increases over Arctic coastal lands in proximity to BKS ice loss. Two major ROS events during November 2006 and 2013 led to massive winter reindeer mortality episodes on the Yamal Peninsula. Fieldwork with migratory herders has revealed that the ecological and socio-economic impacts from the catastrophic 2013 event will unfold for years to come. The suggested link between sea ice loss, more frequent and intense ROS events and high reindeer mortality has serious implications for the future of tundra Nenets nomadism.

A novel approach for the characterization of tundra wetland regions with C-band SAR satellite data.

A circumpolar representative and consistent wetland map is required for a range of applications ranging from upscaling of carbon fluxes and pools to climate modelling and wildlife habitat assessment. Currently available data sets lack sufficient accuracy and/or thematic detail in many regions of the Arctic. Synthetic aperture radar (SAR) data from satellites have already been shown to be suitable for wetland mapping. Envisat Advanced SAR (ASAR) provides global medium-resolution data which are examined with particular focus on spatial wetness patterns in this study. It was found that winter minimum backscatter values as well as their differences to summer minimum values reflect vegetation physiognomy units of certain wetness regimes. Low winter backscatter values are mostly found in areas vegetated by plant communities typically for wet regions in the tundra biome, due to low roughness and low volume scattering caused by the predominant vegetation. Summer to winter difference backscatter values, which in contrast to the winter values depend almost solely on soil moisture content, show expected higher values for wet regions. While the approach using difference values would seem more reasonable in order to delineate wetness patterns considering its direct link to soil moisture, it was found that a classification of winter minimum backscatter values is more applicable in tundra regions due to its better separability into wetness classes. Previous approaches for wetland detection have investigated the impact of liquid water in the soil on backscatter conditions. In this study the absence of liquid water is utilized. Owing to a lack of comparable regional to circumpolar data with respect to thematic detail, a potential wetland map cannot directly be validated; however, one might claim the validity of such a product by comparison with vegetation maps, which hold some information on the wetness status of certain classes. It was shown that the Envisat ASAR-derived classes are related to wetland classes of conventional vegetation maps, indicating its applicability; 30% of the land area north of the treeline was identified as wetland while conventional maps recorded 1-7%.

Detection of snow surface thawing and refreezing in the Eurasian Arctic with QuikSCAT: implications for reindeer herding.

Snow conditions play an important role for reindeer herding. In particular, the formation of ice crusts after rain-on-snow (ROS) events or general surface thawing with subsequent refreezing impedes foraging. Such events can be monitored using satellite data. A monitoring scheme has been developed for observation at the circumpolar scale based on data from the active microwave sensor SeaWinds on QuikSCAT (Ku-band), which is sensitive to changes on the snow surface. Ground observations on Yamal Peninsula were used for algorithm development. Snow refreezing patterns are presented for northern Eurasia above 60 degrees N from autumn 2001 to spring 2008. Western Siberia is more affected than Central and Eastern Siberia in accordance with climate data, and most events occur in November and April. Ice layers in late winter have an especially negative effect on reindeer as they are already weakened. Yamal Peninsula is located within a transition zone between high and low frequency of events. Refreezing was observed more than once a winter across the entire peninsula during recent years. The southern part experienced refreezing events on average four times each winter. Currently, herders can migrate laterally or north-south, depending on where and when a given event occurs. However, formation of ice crusts in the northern part of the peninsula may become as common as they are now in the southern part. Such a development would further constrain the possibility to migrate on the peninsula.

Temporal Stability of Soil Moisture and Radar Backscatter Observed by the Advanced Synthetic Aperture Radar (ASAR).

The high spatio-temporal variability of soil moisture is the result of atmosphericforcing and redistribution processes related to terrain, soil, and vegetation characteristics.Despite this high variability, many field studies have shown that in the temporal domainsoil moisture measured at specific locations is correlated to the mean soil moisture contentover an area. Since the measurements taken by Synthetic Aperture Radar (SAR)instruments are very sensitive to soil moisture it is hypothesized that the temporally stablesoil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT AdvancedSynthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located inthe Duero basin, Spain. It is found that a time-invariant linear relationship is well suited forrelating local scale (pixel) and regional scale (50 km) backscatter. The observed linearmodel coefficients can be estimated by considering the scattering properties of the terrainand vegetation and the soil moisture scaling properties. For both linear model coefficients,the relative error between observed and modelled values is less than 5 % and thecoefficient of determination (R²) is 86 %. The results are of relevance for interpreting anddownscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT)and passive (SMOS, AMSR-E) instruments.