In order to monitor changes in the glaciers in the Bhutan Himalaya, a repeat decadal glacier inventory was carried out from Landsat images of 1977/78 (~1980), 1990, 2000 and 2010. The base map of glaciers was obtained by the object-based image classification method using the multispectral Landsat images of 2010. This method is used separately to delineate clean-ice and debris-covered glaciers with some manual editing. Glacier polygons of 2000,1990 and ~1980 were obtained by manual editing on 2010 by separately overlaying respective years. The 2010 inventory shows 885 glaciers with a total area of ~642 ± 16.1 km2. The glacier area is 1.6% of the total land cover in Bhutan. The result of a repeat inventory shows 23.3 ± 0.9% glacial area loss between ~1980 and 2010, with the highest loss (11.6 ±1.2%) between ~1980 and 1990 and the lowest (6.7 ±0.1%) between 2000 and 2010. The trend of glacier area change from the 1980s to 2010 is -6.4 ± 1.6%. Loss of glacier area was mostly observed below 5600 m a.s.l. and was greater for clean-ice glaciers. The equilibrium-line altitude has shifted upward from 5170 ± 110 m a.s.l. to 5350 ± 150 m a.s.l. in the years ~1980-2010.

Contemporary glaciers of southeast Siberia are located on three high-mountain ridges (east Sayan, Baikalsky and Kodar). In this study, we present an updated glacier inventory based on high- to middle-resolution satellite imagery and field investigations. The inventory includes 51 glaciers with a total area of - 15 km2. Areas of individual glaciers vary from 0.06 to 1.33 km2, lengths from 130 to 2010 m and elevations from 1796 to 3490 m. The recent ice maximum extents (Little Ice Age) have been delineated from terminal moraines. On average, debris-free surface area shrunk by 59% between 1850 and 2006/11 (0.37% a–1), by 44% between 1850 and 2001/02 (0.29% a–1) and by 27% between 2001/02 and 2006/11 (3.39% a–1). The Kodar glaciers have experienced the largest area shrinkage, while the area loss on Baikalsky ridge was more moderate. Glacier changes are mainly related to regional summer temperature increase (by 1.7-2.6C from 1970 to 2010). There are some differences in glacier response due to different spatial patterns of snow accumulation, local topography (e.g. glacier elevation, slope) and geological activity. The studied glaciers (especially of Kodar ridge) are the most sensitive in Siberia to climate change since the late 20th century.

This paper examines the topographic and geometric controls on glacier changes in area and equilibrium-line altitude (ELA) in the central Tien Shan, China, since the Little Ice Age (LIA). We delineate the extents of 487 modern glaciers and their corresponding maximum LIA glacial advances using satellite imagery in Google Earth, and analyze the relationships between the magnitude of glacier changes and a set of local topographic/geometric factors including glacier area, slope, aspect, shape, hypsometry and mean elevation. Our results show that: (1) glacier area decreased from 460.2 km2 during the LIA to 265.6 km2 in the 2000s (a loss of 42.3%), with an average ELA increase of ~100m; (2) relative area changes of glaciers are strongly affected by two of these local factors (glacier area and mean elevation); and (3) ELA change does not show a strong relationship with local factors, suggesting that it may be controlled mainly by climatic factors. This study provides important insights into the local controls on glacier changes at the centennial timescale, which are of critical importance to assess future glacier changes in this arid and semi-arid region.

Glaciers in the Qilian mountains, located in the northeastern part of the Tibetan Plateau, constitute an important freshwater resource for downstream populations and natural systems. To enhance our understanding of the variability of the glaciers, temporally and spatially comprehensive information on them is needed. In this study, the glacier outlines of ~1990, ~2000 and ~2010 for the whole area were delineated in a semi-automated manner using band TM3/TM5 ratio images of Landsat ETM+ or TM scenes with the help of a merged ASTER GDEM/SRTM v4.1 digital elevation model. Combining our own results with those of previously published studies that span the period back to 1956, we found that the glacier area shrank by 30 ±8% from 1956 to 2010 and the shrinkage accelerated remarkably in the past two decades. The linear trends of annual air temperature and precipitation measured at weather stations within the glacierized areas were 0.03-0.05°Ca-1 (significant only after 2000) and 0.37-1.58 mm a–1 (not significant) respectively from 1961 to 2010. Glaciers shrank mainly due to the increasing temperature. Glaciers in the Qilian mountains are very unlikely to have experienced positive mass balance over the past decade. Moreover, given the trend toward higher temperatures, the glaciers in this region will continue to shrink.

The aim of this research is to investigate elevation changes in the Antarctic ice sheet by comparing two digital elevation models (DEMs) derived from satellite altimetry data covering the period 1994–2004. Data collected by ERS-1/2 satellite radar altimetry and by NASA GLAS/ICESat laser altimetry were used. After preprocessing and resampling at the same spatial resolution, both DEMs were compared in a pointwise fashion and elevation differences computed, which consisted of three main components: (1) actual elevation change, (2) errors in the original data sources and (3) interpolation errors that arose during generation of the DEMs. The objectives of the research were to analyze errors, attempt to mitigate systematic effects when possible, and draw some conclusions about the limitations of using DEM products for computing ice-sheet elevation change at local and continental scales. A linear correlation between errors in elevation differences and surface slope was found in the slope range [0°, 0.4°]. This trend was interpreted as residual slope-induced systematic error and compensated for. Finally, an elevation difference map of the Antarctic ice sheet was generated. Analysis of the derived elevation changes at the drainage basin was also made. Results are compared with the results of previous studies.

Airborne and spaceborne remote sensing of ice freeboard offers a good method of retrieving ice thickness in the polar oceans. However, its accuracy is highly limited by the factors altering the hydrostatic equilibrium of ice floes, such as snow cover and melt ponds which change the surface loading on the ice volume. In contrast to the abundant studies on snow loads, little attention has been paid to the role of melt ponds, partly owing to the difficulties of freeboard measurements during the melt season. To help fill this gap and provide a basis for possible instruments and algorithms being able to access ice freeboard with melting surface in future, a theoretical model was developed to investigate the uncertainty in ice thickness retrieval due to surface melting. First, the ice thickness was related to the freeboard, snow depth, melt pond size and densities of snow, ice and water, and then a sensitivity analysis was carried out to study the influence of melt pond morphology. The results show that melting ice has a much lower mean thickness than ice without a melting surface, although with the same freeboard because of a loss of floe weight due to melting. During pond evolution, a floe gains weight when ponds deepen on the vertical scale, but loses weight when they widen on the horizontal scale, resulting in increasing mean ice thickness with decreasing pond depth and fraction. Freeboard is found to be the major source of uncertainty in the retrieved thickness of first-year ice (FYI), while it is ice density in the case of multi-year ice (MYI). The ratio of ice draft to freeboard ranges from 3.0 to 6.2 for FYI and 2.0 to 4.1 for MYI, agreeing with field observations during melting seasons.

The Tibetan Plateau interior area (TPIA), often termed the Qangtang Plateau, is distinguished by many dome-like mountains higher than 6000 ma.s.l. These mountains provide favourable conditions for the development of ice caps and glaciers of extreme continental/subpolar type. According to historical topographic maps (1959–80) and recent Landsat images (2004–11), continuous retreat was observed and the glacierized part of this area decreased by 9.5% (0.27% a–1) with respect to the total glacier area of 8036.4 km2 in the 1970s. Glaciers in the Zhari Namco basin have experienced the highest area shrinkage, with a reduction rate of 0.72% a–1, while the smallest reduction occurred in Bangong Co (0.12% a–1) and Dogai Coying basins (0.11% a–1). A regional gradient of area loss was found, with a larger decrease in the south and a smaller decrease in the north of the plateau. Comparisons indicate glaciers have experienced smaller shrinkage in the TPIA than in surrounding regions. Glacier shrinkage in the TPIA is mainly attributed to an increase in air temperature, while precipitation, glacier size and positive difference of glaciation also played an important role.

This paper provides the results of semi-distributed positive degree-day (PDD) modelling for a glacierized river basin in Nepal. The main objective is to estimate the present and future discharge from the glacierized Langtang River basin using a PDD model (PDDM). The PDDM is calibrated for the period 1993–98 and is validated for the period 1999–2006 with Nash–Sutcliffe values of 0.85 and 0.80, respectively. Furthermore, the projected precipitation and temperature data from 2010 to 2050 are obtained from the Bjerknes Centre for Climate Research, Norway, for the representative concentration pathway 4.5 (RCP4.5) scenario. The Weather Research and Forecasting regional climate model is used to downscale the data from the Norwegian Earth System Model general circulation model. Projected discharge shows no significant trend, but in the future during the pre-monsoon period, discharge will be high and the peak discharge will be in July whereas it is in August at present. The contribution of snow and ice melt from glaciers and snowmelt from rocks and vegetation will decrease in the future: in 2040–50 it will be just 50% of the total discharge. The PDDM is sensitive to monthly average temperature, as a 2°C temperature increase will increase the discharge by 31.9%. Changes in glacier area are less sensitive, as glacier area decreases of 25% and 50% result in a change in the total discharge of –5.7% and –11.4%, respectively.

Accelerating loss of glacial mass caused by rising global temperatures has significant implications. The global cycle of the greenhouse gas CO2 is also associated with mineral weathering and glaciation. In glaciated areas, most estimates of atmospheric CO2 consumption are confined to chemical ionic mass balance or analog modeling methods. We applied the gradient method to the surface of Koxkar glacier, western Tien Shan, China, for a 5 month period during 2012. The overall net glacier-system CO2 exchange (NGE) rate was measured as -0.05 and -0.07mmol m–2 s–1 for regions of exposed ice and supraglacial moraine, respectively. This suggests that atmospheric CO2 drawdown may occur during ice melting because of consumption of H+ by CO2 hydrolysis that occurs in solutions. Using the degree-day model to calculate glacier ablation in bare-ice regions and considering characteristics of the NGE rate in the supraglacial debris region with the support of GIS, the daily NGE rate was estimated to be -1.23 ± 0.17μmmol m–2 d–1 between Julian days 125 and 268 of 2012. These findings present a new approach for modeling the dynamics of glacial CO2 sinks undergoing melting, and develop an understanding of the mechanism of atmospheric CO2 exchange.

Water level fluctuations of inland lakes are related to regional-scale climate changes, and reflect variations in evaporation, precipitation and glacier meltwater flowing into the lake area in its catchment. In this paper, Ice, Cloud and land Elevation Satellite (ICESat) altimeter data and Landsat imagery (2002-09) are used to estimate Nam Co lake (Nyainqentanglha range, Tibetan Plateau) water elevation changes during 2002-09. In 2003 Nam Co lake covered an area of ~1998.8 ± 4.2 km2 and was situated at 4723 m a.s.l. Over such inland water bodies, ICESat altimeter data offer both wide coverage and spatial and temporal accuracy. We combine remote-sensing and GIS technology to map and reconstruct lake area and increased volume changes during a 7 year time series. Nam Co lake water level increased by 2.4±0.12m (0.33ma–1) between 23 February 2003 and 1 October 2009, and lake volume increased by 4.9 ±0.5 km3. In the past 7 years, Nam Co lake area has increased from 1998.78 ±5.4 to 2023.8 ±3.4 km2, the glacier-covered area has decreased from 832.34 to 821.0 km2 and the drainage basin area has decreased from 201.1 ±4.2 to 196.1 ±2.3 km2. However, the most spectacular feature is the continual water level rise from 2003 to 2009 without an obvious associated increase in precipitation. Based on digital elevation models (DEMs) from Shuttle Radar Topography Mission (SRTM) DEM data and corrected ICESat elevation data, significant changes to glacier mass balance in the western Nyainqentanglha mountains are indicated. Nyainqentanglha mountain glacier surface elevations decreased by 8.39 ± 0.45 m during 2003-09. Over the same period, at least 1.01 km3 of glacial meltwater flowed into Nam Co lake, assuming a glacial runoff coefficient of 0.6. The mean glacier mass-balance value is -490mmw.e. over the corresponding period, indicating that glacier meltwater in the catchment contributes to lake level rise. The contribution rate of glacial meltwater to lake water volume rise is 20.75%. The temporal lake level fluctuation correlates with temperature variations over the same time span.

Characterized by fast movement, low surface slope and grounding below sea level, Pine Island Glacier (PIG) plays an important role in the stability of the West Antarctic ice sheet. In previous work, we reported that the spatial distribution of 1995–2003 surface lowering in PIG suggests an attribution of changes to an internally forced process in the glacier. Other work associates changes in PIG entirely with processes in its ice shelf. Here time series of maps of surface elevation change in PIG and its ice shelf are derived from geostatistical analysis of ICESat GLAS and ERS-1 radar altimeter data. Based on spatio-temporal analysis of 1995–2007 elevation change, we discuss indications of processes that initiate from changes in the ice shelf versus processes that start internally in the glacier. Thinning rates continued to increase after 2003, regionally to >15 m a–1. The initiation of acceleration occurred in the interior of the ice stream, while in later years largest elevation loss was driven by changes in the ice shelf and upward propagation. By 2006, the region of thinning had expanded up-glacier beyond the initial areas of surface lowering to 100 km above the hinge line. More than one process causes dynamically complex changes in PIG.

Based on the results of geochemical and glaciological investigations in snow pits and shallow cores, regional stack series of air temperature in central Antarctica (in the southern part of Vostok Subglacial Lake) were obtained, covering the last 350 years. It is shown that this parameter varied quasi-periodically with a wavelength of 30–50 years. The correlation of the newly obtained record with the circulation indices of the Southern Hemisphere (SH) shows that the central Antarctic climate is mainly governed by the type of circulation in the SH: under conditions of zonal circulation, negative anomalies of temperature and precipitation rate are observed, whereas the sign of the anomalies is positive during meridional circulation. In the 1970s the sign of the relationship between many climatic parameters changed, which is likely related to the rearrangement of the climatic system of the SH. The data suggest that during the past 350 years such events have taken place at least five times. The stable water isotope content of the central Antarctic snow is governed by the summer temperature rather than the mean annual temperature, which is interpreted as the influence of ‘postdepositional’ effects.