Glacier mass loss is recognized as a major contributor to current sea level rise. However, large uncertainties remain in projections of glacier mass loss on global and regional scales. We present an ensemble of 288 glacier mass and area change projections for the 21st century based on 11 glacier models using up to 10 general circulation models and four Representative Concentration Pathways (RCPs) as boundary conditions. We partition the total uncertainty into the individual contributions caused by glacier models, general circulation models, RCPs, and natural variability. We find that emission scenario uncertainty is growing throughout the 21st century and is the largest source of uncertainty by 2100. The relative importance of glacier model uncertainty decreases over time, but it is the greatest source of uncertainty until the middle of this century. The projection uncertainty associated with natural variability is small on the global scale but can be large on regional scales. The projected global mass loss by 2100 relative to 2015 (79 ± 56 mm sea level equivalent for RCP2.6, 159 ± 86 mm sea level equivalent for RCP8.5) is lower than, but well within, the uncertainty range of previous projections.

We present a new glacier inventory for high-mountain Asia named "Glacier Area Mapping for Discharge from the Asian Mountains" (GAMDAM). Glacier outlines were delineated manually using 356 Landsat ETM+ scenes in 226 path-row sets from the period 1999–2003, in conjunction with a digital elevation model (DEM) and high-resolution Google Earth^TM imagery. Geolocations are largely consistent between the Landsat imagery and DEM due to systematic radiometric and geometric corrections made by the United States Geological Survey. We performed repeated delineation tests and peer review of glacier outlines in order to maintain the consistency and quality of the inventory. Our GAMDAM glacier inventory (GGI) includes 87 084 glaciers covering a total area of 91 263 ± 13 689 km² throughout high-mountain Asia. In the Hindu Kush–Himalaya range, the total glacier area in our inventory is 93% that of the ICIMOD (International Centre for Integrated Mountain Development) inventory. Discrepancies between the two regional data sets are due mainly to the effects of glacier shading. In contrast, our inventory represents significantly less surface area (−24%) than the recent global Randolph Glacier Inventory, version 4.0 (RGI), which includes 119 863 ± 9201 km² for the entirety of high Asian mountains. Likely causes of this disparity include headwall definition, effects of exclusion of shaded glacier areas, glacier recession since the 1970s, and inclusion of seasonal snow cover in the source data of the RGI, although it is difficult to evaluate such effects quantitatively. Further rigorous peer review of GGI will both improve the quality of glacier inventory in high-mountain Asia and provide new opportunities to study Asian glaciers.

Recent studies of Asian glaciers have shown that glaciers in eastern Karakoram and West Kunlun have been slightly gaining mass while those in nearby Jammu Kashmir and Himalayas are losing mass, at rates of more than 0.5 m w.e.yr⁻¹ and about 0.3 m w.e.yr⁻¹, respectively. Two possible explanations have been proposed for this difference in glacier behaviour: spatial heterogeneity in climate change (climatic forcing) or differing glacier responses to climate change (glacier response). However, neither explanation has strong supporting evidence. Here, we examine the glacial response by calculating the mass-balance sensitivity to temperature change in high-mountain Asia. In support of the glacier-response explanation, we find a strong correlation between observed glacier surface-elevation changes and mass-balance sensitivity of glaciers. The high coefficient of determination (R² = 0.61) suggests that spatially heterogeneous mass-balance sensitivity has more explanatory power than regionally different climate change for the recent contrasting glacier fluctuations in the high mountain Asia.

Glacial lakes are potentially dangerous sources of glacial lake outburst floods (GLOFs), and represent a serious natural hazard in Himalayan countries. Despite the development of various indices aimed at determining the outburst probability, an objective evaluation of the thousands of Himalayan glacial lakes has yet to be completed. In this study we propose a single index, based on the depression angle from the lakeshore, which allows the lakes to be assessed using remotely sensed digital elevation models (DEMs). We test our approach on five lakes in Nepal, Bhutan, and Tibet using images taken by the declassified Hexagon KH-9 satellite before these lakes experienced an outburst flood. All five lakes had a steep lakefront area (SLA), on which a depression angle was steeper than our proposed threshold of 10° before the GLOF event, but the SLA was no longer evident after the events. We further calculated the potential flood volume (PFV); i.e., the maximum volume of floodwater that could be released if the lake surface was lowered sufficiently to eradicate the SLA. This approach guarantees repeatability to assess the possibility of GLOF hazards because it requires no particular expertise to carry out, though the PFV does not quantify the GLOF risk. We calculated PFVs for more than 2000 Himalayan glacial lakes using visible band images and DEMs of ASTER data. The PFV distribution follows a power-law function. We found that 794 lakes did not have an SLA, and consequently had a PFV of zero, while we also identified 49 lakes with PFVs of over 10 million m³, which is a comparable volume to that of recorded major GLOFs. This PFV approach allows us to preliminarily identify and prioritize those Himalayan glacial lakes that require further detailed investigation on GLOF hazards and risk.

Although the processes by which glacial debris mantles alter the melting of glacier ice have been well studied, the mass balance and runoff patterns of Himalayan debris-covered glaciers and the response of these factors to climate change are not well understood. Many previous studies have addressed mechanisms of ice melt under debris mantles by applying multiplicative parameters derived from field experiments, and other studies have calculated the details of heat conduction through the debris layer. However, those approaches cannot be applied at catchment scale because distributions of thickness and thermal property of debris are heterogeneous and difficult to measure. Here, we established a runoff model for a Himalayan debris-covered glacier in which the spatial distribution of the thermal properties of the debris mantle is estimated from remotely sensed multi-temporal data. We applied the model to the Tsho Rolpa Glacial Lake–Trambau Glacier basin in the Nepal Himalaya, using hydro-meteorological observations obtained for a 3.5-year period (1993–1996). We calculated long-term averages of runoff components for the period 1980–2007 using gridded reanalysis datasets. Our calculations suggest that excess meltwater, which implies the additional water runoff compared with the ice-free terrain, from the debris-covered area contributes significantly to the total runoff, mainly because of its location at a lower elevation. Uncertainties in runoff simulation due to estimations of the thermal properties and albedo of the debris-covered surface were assessed to be approximately 8% of the runoff from the debris-covered area. We evaluated the sensitivities of runoff components to changes in air temperature and precipitation. As expected, warmer air temperatures increase the total runoff by increasing the melting rate; however, increased precipitation slightly reduces the total runoff, as ice melting is suppressed by the increased snow cover and associated high albedo. The response of total runoff to changing precipitation is complex because of the different responses of individual components (glacier, debris, and ice-free terrain) to precipitation.