Recent Surge Behavior of Walsh Glacier Revealed by Remote Sensing Data Many surgetype glaciers are present on the St. Elias Mountains, but a detailed study on the surge behavior of the glaciers is still missing. In this study, we used remote sensing data to reveal detailed glacier surge behavior, focusing on the recent surge at Walsh Glacier, which was reported to have surged once in the 1960s. Glacial velocities were derived using a crosscorrelation algorithm, and changes in the medial moraines were interpreted based on Landsat images. The digital elevation model (DEM) difference method was applied to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs to evaluate the surface elevation of the glacier. The results showed that the surge initiated near the conjunction of the eastern and northern branches, and then quickly spread downward. The surge period was almost three years, with an active phase of less than two years. The advancing speed of the surge front was much large than the maximum ice velocity of xe2x89x8814 md observed during the active phase. Summer speedups and a winter speedup in ice velocity were observed from velocity data, with the speedups being more obvious during the active phase. Changes in the glacier velocity and the medial moraines suggested that the eastern branch was more affected by the surge. The DEM differencing results showed that the receiving zone thickened up to about 140 m, and the upstream reservoir zone became thinner. These surge behaviors, as characterized by remote sensing data, gave us more detailed insights into the surge dynamics of Walsh Glacier.

How much variation in land surface phenology can climate oscillation modes explain at the scale of mountain pastures in Kyrgyzstan Abstract   Climate oscillation modes can shape weather across the globe due to atmospheric teleconnections. We built on the findings of a recent study to assess whether the impacts of teleconnections are detectable and significant in the early season dynamics of highland pastures across five rayons in Kyrgyzstan. Specifically, since land surface phenology (LSP) has already shown to be influenced by snow cover seasonality and terrain, we investigated here how much more explanatory and predictive power information about climatic oscillation modes might add to explain variation in LSP. We focused on seasonal values of five climate oscillation indices that influence vegetation dynamics in Central Asia. We characterized the phenology in highland pastures with metrics derived from LSP modeling using Landsat NDVI time series together with MODIS land surface temperature (LST) data: Peak Height (PH), the maximum modeled NDVI and Thermal Time to Peak (TTP), the quantity of accumulated growing degreedays based on LST required to reach PH. Next, we calculated two metrics of snow cover seasonality from MODIS snow cover composites: last date of snow (LDoS), and the number of snow covered dates (SCD). For terrain features, we derived elevation, slope, and TRASP index as linearization of aspect. First, we used Spearmanxe2x80x99s rank correlation to assess the geographical differentiation of land surface phenology metrics responses to environmental variables. PH showed weak correlations with TTP (positive in western but negative in eastern rayons), and moderate relationships with LDoS and SCD only in one northeastern rayon. Slope was weakly related to PH, while TRASP showed a consistent moderate negative correlation with PH. A significant but weak negative correlation was found between PH and SCAND JJA, and a significant weak positive correlation with MEI MAM. TTP showed consistently strong negative relationships with LDoS, SCD, and elevation. Very weak positive correlations with TTP were found for EAWR DJF, AMO DJF, and MEI DJF in western rayons only. Second, we used Partial Least Squares regression to investigate the role of oscillation modes altogether. PLS modelling of TTP showed that thermal time accumulation could be explained mostly by elevation and snow cover metrics, leading to reduced models explaining 55 to 70% of observed variation in TTP. Variable selection indicated that NAO JJA, AMO JJA and SCAND MAM had significant relationships with TTP, but their input of predictive power was neglible. PLS models were able to explain up to 29% of variability in PH. SCAND JJA and MEI MAM were shown to be significant predictors, but adding them into models did not influence modeling performance. We concluded the impacts of climate oscillation anomalies were not detectable or significant in mountain pastures using LSP metrics at fine spatial resolution. Rather, at a 30xe2x80xafm resolution, the indirect effects of seasonal climatic oscillations are overridden by terrain influences (mostly elevation) and snow cover timing. Whether climate oscillation mode indices can provide some new and useful information about growing season conditions remains a provocative question, particularly in light of the multiple environmental challenges facing the agropastoralism livelihood in montane Central Asia.