Elevation lines of modelled ice sheet extension during last glacial maximum

A numerical ice-sheet model was used to reconstruct the Late Weichselian glaciation of the Eurasian High Arctic, between Franz Josef Land and Severnaya Zemlya. An ice sheet was developed over the entire Eurasian High Arctic so that ice flow from the central Barents and Kara seas toward the northern Russian Arctic could be accounted for. An inverse approach to modeling was utilized, where ice-sheet results were forced to be compatible with geological information indicating ice-free conditions over the Taymyr Peninsula during the Late Weichselian. The model indicates complete glaciation of the Barents and Kara seas and predicts a "maximum-sized" ice sheet for the Late Weichselian Russian High Arctic. In this scenario, full-glacial conditions are characterized by a 1500-m-thick ice mass over the Barents Sea, from which ice flowed to the north and west within several bathymetric troughs as large ice streams. In contrast to this reconstruction, a "minimum" model of glaciation involves restricted glaciation in the Kara Sea, where the ice thickness is only 300 m in the south and which is free of ice in the north across Severnaya Zemlya. Our maximum reconstruction is compatible with geological information that indicates complete glaciation of the Barents Sea. However, geological data from Severnaya Zemlya suggest our minimum model is more relevant further east. This, in turn, implies a strong paleoclimatic gradient to colder and drier conditions eastward across the Eurasian Arctic during the Late Weichselian.

Distribution models for mountain plant species: The value of elevation

The climatic conditions of mountain habitats are greatly influenced by topography. Large differences in microclimate occur with small changes in elevation, and this complex interaction is an important determinant of mountain plant distributions. In spite of this, elevation is not often considered as a relevant predictor in species distribution models (SDMs) for mountain plants. Here, we evaluated the importance of including elevation as a predictor in SDMs for mountain plant species. We generated two sets of SDMs for each of 73 plant species that occur in the Pacific Northwest of North America; one set of models included elevation as a predictor variable and the other set did not. AUC scores indicated that omitting elevation as a predictor resulted in a negligible reduction of model performance. However, further analysis revealed that the omission of elevation resulted in large over-predictions of species' niche breadths-this effect was most pronounced for species that occupy the highest elevations. In addition, the inclusion of elevation as a predictor constrained the effects of other predictors that superficially affected the outcome of the models generated without elevation. Our results demonstrate that the inclusion of elevation as a predictor variable improves the quality of SDMs for high-elevation plant species. Because of the negligible AUC score penalty for over-predicting niche breadth, our results support the notion that AUC scores alone should not be used as a measure of model quality. More generally, our results illustrate the importance of selecting biologically relevant predictor variables when constructing SDMs.

Static GPS measurements and elevation model in the hinterland of Neumayer

As part of the CryoSat Cal/Val activities and the pre-site survey for an ice core drilling contributing to the International Partnerships in Ice Core Sciences (IPICS), ground based kinematic GPS measurements were conducted in early 2007 in the vicinity of the German overwintering station Neumayer (8.25° W and 70.65° S). The investigated area comprises the regions of the ice tongues Halvfarryggen and Søråsen, which rise from the Ekströmisen to a maximum of about 760 m surface elevation, and have an areal extent of about 100 km x 50 km each. Available digital elevation models (DEMs) from radar altimetry and the Antarctic Digital Database show elevation differences of up to hundreds of meters in this region, which necessitated an accurate survey of the conditions on-site. An improved DEM of the Ekströmisen surroundings is derived by a combination of highly accurate ground based GPS measurements, satellite derived laser altimetry data (ICESat), airborne radar altimetry (ARA), and radio echo sounding (RES). The DEM presented here achieves a vertical accuracy of about 1.3 m and can be used for improved ice dynamic modeling and mass balance studies.