Approximately 25 % of the glacierized area in the Everest region is covered by debris, yet the surface mass balance of debris-covered portions of these glaciers has not been measured directly. In this study, ground-based measurements of surface elevation and ice depth are combined with terrestrial photogrammetry, unmanned aerial vehicle (UAV) and satellite elevation models to derive the surface mass balance of the debris-covered tongue of Changri Nup Glacier, located in the Everest region. Over the debris-covered tongue, the mean elevation change between 2011 and 2015 is −0.93 m year⁻¹ or −0.84 m water equivalent per year (w.e. a⁻¹). The mean emergence velocity over this region, estimated from the total ice flux through a cross section immediately above the debris-covered zone, is +0.37 m w.e. a⁻¹. The debris-covered portion of the glacier thus has an area-averaged mass balance of −1.21 ± 0.2 m w.e. a⁻¹ between 5240 and 5525 m above sea level (m a.s.l.). Surface mass balances observed on nearby debris-free glaciers suggest that the ablation is strongly reduced (by ca. 1.8 m w.e. a⁻¹) by the debris cover. The insulating effect of the debris cover has a larger effect on total mass loss than the enhanced ice ablation due to supraglacial ponds and exposed ice cliffs. This finding contradicts earlier geodetic studies and should be considered for modelling the future evolution of debris-covered glaciers.

Ice cliff backwasting on debris-covered glaciers is recognized as an important mass-loss process that is potentially responsible for the “debris-cover anomaly”, i.e. the fact that debris-covered and debris-free glacier tongues appear to have similar thinning rates in the Himalaya. In this study, we quantify the total contribution of ice cliff backwasting to the net ablation of the tongue of Changri Nup Glacier, Nepal, between 2015 and 2017. Detailed backwasting and surface thinning rates were obtained from terrestrial photogrammetry collected in November 2015 and 2016, unmanned air vehicle (UAV) surveys conducted in November 2015, 2016 and 2017, and Pléiades tri-stereo imagery obtained in November 2015, 2016 and 2017. UAV- and Pléiades-derived ice cliff volume loss estimates were 3 % and 7 % less than the value calculated from the reference terrestrial photogrammetry. Ice cliffs cover between 7 % and 8 % of the total map view area of the Changri Nup tongue. Yet from November 2015 to November 2016 (November 2016 to November 2017), ice cliffs contributed to 23±5 % (24±5 %) of the total ablation observed on the tongue. Ice cliffs therefore have a net ablation rate 3.1±0.6 (3.0±0.6) times higher than the average glacier tongue surface. However, on Changri Nup Glacier, ice cliffs still cannot compensate for the reduction in ablation due to debris-cover. In addition to cliff enhancement, a combination of reduced ablation and lower emergence velocities could be responsible for the debris-cover anomaly on debris-covered tongues.

As the highest mountain on Earth, Mount Everest is an iconic peak that offers an unrivalled natural platform for measuring ongoing climate change across the full elevation range of Asia’s water towers. However, Everest’s extreme environment challenges data collection, particularly on the mountain’s upper slopes, where glaciers accumulate mass and mountaineers are most exposed. Weather stations have operated on Everest before, including the world’s previous highest, but coverage has been sparse in space and time. Here we describe the installation of a network of five automatic weather stations (AWSs), including the two highest stations on Earth (8,430 and 7,945 m MSL) which greatly improves monitoring of this iconic mountain. We highlight sample applications of the new data, including an initial assessment of surface energy fluxes at Camp II (6,464 m MSL) and the South Col (7,945 m MSL), which suggest melt occurs at both sites, despite persistently below-freezing air temperatures. This analysis indicates that melt may even be possible at the 8,850 m MSL summit, and prompts a reevaluation of empirical temperature index models used to simulate glacier melt in the Himalayas that focus only on air temperature. We also provide the first evaluation of numerical weather forecasts at almost 8,000 m MSL and use of model output statistics to reduce forecast error, showcasing an important opportunity to improve climber safety on Everest. Looking forward, we emphasize the considerable potential of these freely available data for understanding weather and climate in the Himalayas and beyond, including tracking the behavior of upper-atmosphere winds, which the AWS network is uniquely positioned to monitor.

This study aims to assess the accuracy of two satellite‐based precipitation products (SBPPs), that is, Tropical Rainfall Measurement Mission (TRMM)‐based Multi‐satellite Precipitation Analysis (TMPA) and its upgraded version Integrated Multi‐Satellite Retrievals for Global Precipitation Measurement (IMERG), in capturing spatial and temporal variation of precipitation and their application for extreme events (high‐intensity precipitation and drought). They were evaluated against 142‐gauge stations from Nepal during 2001–2018. The results show that, in general, both SBPPs show the overall characteristics of precipitation patterns, although underestimated the mean annual precipitation during the study period. It was also noted that IMERG product yields better performance to detect precipitation events (probability of detection) and no‐precipitation events (false alarm ratio) than TMPA. Based on four different extreme precipitation indices: heavy precipitation events (R10mm), extreme precipitation events (R25mm), five consecutive dry days (CDD), and five consecutive wet days (CWD), it was observed that the SBPPs underestimated the frequency of R25mm and CDD spells while overestimated R10mm and CWD spells. Additionally, both SBPPs exhibited considerable capabilities in capturing the drought events during the study period. Overall, the drought event, bias, and frequency show that the IMERG product has slightly better capabilities to capture drought than the TMPA product. In general, IMERG was found to be superior at a daily timescale, while TMPA shows consistent performance on a monthly scale during the study period. Furthermore, there is still space for further improvement of IMERG rainfall retrieval algorithms.