Glaciers are particularly sensitive to climate change, making them vulnerable elements of the environment. Of potential concern for societies is the rapid glacier retreat of Himalayan glaciers. Besides the vast potential for hydroelectric power, these glaciers contribute to the major rivers in Asia [Immerzeel et al., 2010; Kaser et al., 2006], and glacier meltwater is critical for agriculture in many, especially drier, regions in summer. Beyond, glacial lake outburst floods are one of the major natural hazards in this region, and the hazard potential is rapidly growing as a result of glacier thinning and retreat [Richardson and Raynolds, 2000]. Nevertheless, estimates of ongoing and near-future glacier change in the Himalayas relative to the climate forcing remains poorly quantified and thus highly controversial [e.g.,Cogley et al., 2010; Immerzeel et al., 2010; Jacob et al., 2012; Bolch et al., 2012].
 Recent studies have estimated large-scale Himalayan glacier change through remote sensing techniques. However, the results range from dramatic ice mass loss [e.g.,Dyurgerov et al., 2009; Bolch et al., 2011] with some estimates for high Asia as large as ∼50 Gigatons per year [Matsuo and Heki, 2010], to almost constant glacier mass [Jacob et al., 2012]. These discrepancies could be partially explained by aspects inherent to differing remote sensing approaches, such as short observation periods, uncertainty in the remote sensing algorithms applied, and spatial resolution of the data. Detailed, regional glaciological mass-balance studies integrating multi-decadal glacier change are therefore desirable for comparison to both remote sensing studies as well as local, field-based, mass balance studies. One of the greatest challenges is the severe lack of field data for model and remote sensing validation. This stems, in part, from the immense number of glaciers spread over a vast region, the complex politics of the region, and the rugged terrain. Modeling of glacier mass balance and sensitivity can complement, extend, and motivate field and remote sensing studies of mass balance. Therefore, quantifying the uncertainties that attend model estimates of glacier mass balance and sensitivity in the absence of accurate validation data is critical. From the perspective of a glacier modeling approach to estimating glacier changes, these uncertainties stem from uncertainties in the glacier models, glacierized area, and climate data. Indeed, most studies use a single climate data set to quantify glacier changes and projections [e.g.,Rupper and Roe, 2008; Immerzeel et al., 2010], without quantifying the uncertainties associated with these data. In this study, we assess the uncertainties in projected glacier changes associated with the climate data and derive a conservative scenario for recent and ongoing glacier change in the Kingdom of Bhutan, in the monsoonal Himalayas.
 Bhutan is chosen for several reasons. First, Bhutan exemplifies an area where little data on glacier changes are available and where it is logistically difficult to obtain field-based studies, a common problem for many regions of the Himalayas. Few glaciological studies have addressed glacier changes in Bhutan, with the exceptions of a glacier area inventory [Mool et al., 2001], quantitative estimates of glacier retreat and area decrease from 1963 to 1993 for a sampling of ∼15% of Bhutan's glaciers [Karma et al., 2003] and an atlas of glaciers of Bhutan [Iwata, 2010]. Not all of these studies are peer-reviewed, and the uncertainties associated with the reports are not well known. All of these studies and reports do provide important insights into the rapid changes occurring in the large glacierized areas in Bhutan over the past half century. However, to date there are no published field- or modeling-based mass balance estimates for these glaciers, no estimates of glacier sensitivity to climate in the region, and no estimates of related change in meltwater flux over time. Thus, while there is significant evidence that the glaciers in Bhutan have been retreating over the past half century, we know little about the causes of those glacier changes and what the future glacier change will be.
 Second, glaciers in Bhutan, just as neighboring glaciers in India, Nepal, and Southwest China, sit in the bulls-eye of high snow accumulation glaciers (Figure 1, top) (see Text S1for methods detail). Sensitivity tests using a temperature-melt model (Text S1) support prior work that show that high accumulation regions are extremely temperature-sensitive [e.g.,Rupper and Roe, 2008; Fujita and Nuimura, 2011]. Therefore, Bhutan's glaciers form a highly suitable natural laboratory to investigate glacier sensitivity and response to temperature change in the monsoonal Himalaya (Figure 1).
 Finally, there are socio-economic reasons to focus work on the glacierized regions of Bhutan. The Kingdom of Bhutan is a country at the forefront of climate change mitigation strategies (Bhutan was the first developing country to receive climate mitigation monies from the UN's Least Developed Countries fund), and faces many of the economic and hazard challenges associated with glacier changes in the larger Himalayan region [Nayar, 2009]. For example, Bhutan's main economic export is hydroelectric power and the powerplant safety and viability depends critically on prediction of glacial lake outburst floods and related mitigation measures [Belding and Vokso, 2011]. Therefore, the well-being of Bhutan's society requires accurate estimates of glacier change and meltwater production.
 This work provides a comprehensive study of glacier systems in Bhutan, and the first associated quantification of present and near-future glacierized area loss and related changes in melt water flux with particular emphasis on the sensitivity of these quantifications to the choice of different climate input data sets. We base our investigations on new glacier mapping from satellite imagery, a suite of gridded climate data, and a robust glacier melt model. This approach provides a range in mass balance estimates over the entire glacierized region.