Abstract. The National Geographic Rolex Perpetual Planet Everest expeditions 2019–2022 installed a network of Automatic Weather Stations (AWSs) to improve understanding of the climate at high altitudes in the Nepal Himalaya. This knowledge is critical in the Mount Everest (Khumbu) region, due to its extreme altitude, popularity amongst trekkers and mountaineers, and its importance as a source of freshwater for downstream communities. Here we present quality controlled (QC) meteorological data from six AWSs from Phortse (3,810 m above sea level, m asl) to Bishop Rock (8,810 m asl) in the Everest region, including the seasonal climatology, and a comparison with ERA5 reanalysis data from the South Col AWS. The data is accessible from https://doi.org/10.5281/zenodo.18849098.

Abstract. One of the most extreme snowstorms ever documented, with among the highest 12- and 24-hour liquid-equivalent accumulations recorded globally, was captured by an ultra-high-elevation weather-station network on Mt. Everest on 4 October 2025. Beyond redefining expectations of extreme Himalayan snowfall magnitudes, the event revealed a relationship between precipitation intensity and phase: melting of precipitating hydrometeors cooled the atmosphere and suppressed rain at glacierised elevations, with the low-density, high-altitude atmosphere amplifying this feedback. As the climate warms, more extreme Himalayan precipitation is likely, but whether such events bring rain or snow—and therefore their impacts—will depend on fine-scale storm dynamics.

Glacier mass-balance modelling relies on parameterizations to distribute meteorological variables such as precipitation and air temperature over glacier surfaces. However, meteorological observations at the highest altitudes are sparse, particularly outside of Europe, which presents challenges for glacier modelling in high-altitude regions such as the central Himalaya. This study utilizes a dense network of weather stations in the Khumbu Valley, Nepal, to derive parameterizations for distributing air temperature and precipitation over the Khumbu Glacier. These parameterizations are then compared to those of the GlacierMIP project. This study finds a seasonally varying temperature gradient less negative than those employed by most models, a precipitation gradient which follows an exponential decay and a modelled annual Khumbu Glacier accumulation of 575 +/- 24 mm water equivalent, lower than any of the models surveyed, which overestimate accumulation by between 7% and 41%. Physical, process-based interpretations of the parameterizations suggest that these points of difference with GlacierMIP models are likely common across the central Himalaya. Errors in these parameterizations will lead to errors in modelled glacier responses to climate change.

In a warming climate, glaciers will experience increased liquid precipitation and melt, making it crucial to better understand and model the associated surface processes. This study presents a modeling approach developed to investigate the dynamic interaction between surface liquid water and bare ice using the SURFEX/ISBA-Crocus model. The implementation of the temporary retention of liquid water from rain or melt at the ice surface is described. The water is drained or can refreeze depending on meteorological conditions, directly affecting the albedo, thermal profile and glacier mass balance. This new development, tested to Mera Glacier (Nepal) shows an impact up to 6 % on the annual mass balance with contrasted effects depending on the meteorological conditions. During the pre-monsoon season, this implementation leads to greater mass loss (up to 20 %) due to surface liquid water, which enhances warming rather than compensating through refreezing. During the monsoon and post-monsoon seasons, it leads to less negative mass balance as a result of increased refreezing. Sensitivity analyses identified drainage and albedo as key model parameters. A 10 % change in stored liquid water drainage results in a 10 % change in annual mass balance. The albedo of bare ice and liquid water over ice represent the primary contributors to mass balance loss and the greatest uncertainties, making them priority targets for further investigation and improved characterization. This physically-based model development is essential for future climate projections worldwide, particularly given increasing melt, rainfall, and bare ice exposure under climate change.

We analyze snow water equivalent (SWE) measurements from a cosmic ray sensor (CRS) on the lower accumulation area of Mera Glacier (central Himalaya, Nepal) between November 2019 and November 2021. The CRS aligned well with field observations and revealed accumulation in pre-monsoon and monsoon observations, followed by ablation in post-monsoon and winter observations. COSIPY simulations suggest significant surface melting, water percolation, and refreezing within the snowpack, consistent with CRS observations yet liable to be missed by surface mass balance surveys. We conclude that CRS can be used to determine mass fluxes in various climatic settings, but the interpretation of the total changes in SWE needs complementary measurements and model analysis to determine the share of specific mass fluxes, such as melt and refreezing.

The sensitivity of glacier mass balance to temperature and precipitation variations is crucial for informing models that simulate glaciers’ response to climate change. In this study, we simulate the glacier-wide mass balance of Mera Glacier with a surface energy balance model, driven by in-situ meteorological data, from 2016 to 2020. The analysis of the share of the energy fluxes of the glacier shows the radiative fluxes account for almost all the energy available during the melt season (May to October). However, turbulent fluxes are significant outside the monsoon (June to September). On an annual scale, melt is the dominant mass flux at all elevations, but 44 % of the melt refreezes across the glacier. By reshuffling the available observations, we create 180 synthetic series of hourly meteorological forcings to force the model over a wide range of plausible climate conditions. A +1 (-1)°C change in temperature results in a -0.75 ± 0.17 (+0.93 ± 0.18) m w.e. change in glacier-wide mass balance and a +20 (-20)% change in precipitation results in a +0.52 ± 0.10 (-0.60 ± 0.11) m w.e. change. Our study highlights the need for physically based approaches to produce consistent forcing datasets, and calls for more meteorological and glaciological measurements in High Mountain Asia.

WeatherEarly View Short ArticleOpen Access Insights from the first winter weather observations near Mount Everest's summit Tenzing Chogyal Sherpa, Corresponding Author Tenzing Chogyal Sherpa [email protected] orcid.org/0000-0003-4512-2194 International Centre for Integrated Mountain Development, Kathmandu, Nepal Correspondence to: T. C. Sherpa [email protected]Search for more papers by this authorTom Matthews, Tom Matthews orcid.org/0000-0001-6295-1870 Department of Geography, King's College London, UKSearch for more papers by this authorL. Baker Perry, L. Baker Perry orcid.org/0000-0003-0598-6393 Appalachian State University, Boone, North Carolina, USASearch for more papers by this authorAmrit Thapa, Amrit Thapa International Centre for Integrated Mountain Development, Kathmandu, Nepal Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska, USASearch for more papers by this authorPraveen Kumar Singh, Praveen Kumar Singh Indian Institute of Technology Roorkee, Uttarakhand, IndiaSearch for more papers by this authorArbindra Khadka, Arbindra Khadka orcid.org/0000-0002-8564-1477 International Centre for Integrated Mountain Development, Kathmandu, Nepal University of Grenoble Alpes, CNRS, IRD, IGE, Grenoble, FranceSearch for more papers by this authorInka Koch, Inka Koch Department of Geosciences, University of Tübingen, GermanySearch for more papers by this authorMauri Pelto, Mauri Pelto Department of Environmental Science, Nichols College, Dudley, Massachusetts, USASearch for more papers by this authorPrajjwal Panday, Prajjwal Panday Department of Environmental Science, Nichols College, Dudley, Massachusetts, USASearch for more papers by this authorDeepak Aryal, Deepak Aryal Central Department of Hydrology and Meteorology, Tribhuvan University, Kirtipur, NepalSearch for more papers by this authorDibas Shrestha, Dibas Shrestha Central Department of Hydrology and Meteorology, Tribhuvan University, Kirtipur, NepalSearch for more papers by this authorShichang Kang, Shichang Kang State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, ChinaSearch for more papers by this authorPaul Andrew Mayewski, Paul Andrew Mayewski Climate Change Institute, University of Maine, Orono, Maine, USASearch for more papers by this author Tenzing Chogyal Sherpa, Corresponding Author Tenzing Chogyal Sherpa [email protected] orcid.org/0000-0003-4512-2194 International Centre for Integrated Mountain Development, Kathmandu, Nepal Correspondence to: T. C. Sherpa [email protected]Search for more papers by this authorTom Matthews, Tom Matthews orcid.org/0000-0001-6295-1870 Department of Geography, King's College London, UKSearch for more papers by this authorL. Baker Perry, L. Baker Perry orcid.org/0000-0003-0598-6393 Appalachian State University, Boone, North Carolina, USASearch for more papers by this authorAmrit Thapa, Amrit Thapa International Centre for Integrated Mountain Development, Kathmandu, Nepal Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska, USASearch for more papers by this authorPraveen Kumar Singh, Praveen Kumar Singh Indian Institute of Technology Roorkee, Uttarakhand, IndiaSearch for more papers by this authorArbindra Khadka, Arbindra Khadka orcid.org/0000-0002-8564-1477 International Centre for Integrated Mountain Development, Kathmandu, Nepal University of Grenoble Alpes, CNRS, IRD, IGE, Grenoble, FranceSearch for more papers by this authorInka Koch, Inka Koch Department of Geosciences, University of Tübingen, GermanySearch for more papers by this authorMauri Pelto, Mauri Pelto Department of Environmental Science, Nichols College, Dudley, Massachusetts, USASearch for more papers by this authorPrajjwal Panday, Prajjwal Panday Department of Environmental Science, Nichols College, Dudley, Massachusetts, USASearch for more papers by this authorDeepak Aryal, Deepak Aryal Central Department of Hydrology and Meteorology, Tribhuvan University, Kirtipur, NepalSearch for more papers by this authorDibas Shrestha, Dibas Shrestha Central Department of Hydrology and Meteorology, Tribhuvan University, Kirtipur, NepalSearch for more papers by this authorShichang Kang, Shichang Kang State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, ChinaSearch for more papers by this authorPaul Andrew Mayewski, Paul Andrew Mayewski Climate Change Institute, University of Maine, Orono, Maine, USASearch for more papers by this author First published: 20 March 2023 https://doi.org/10.1002/wea.4374 The views and interpretations in this publication are those of the authors and are not necessarily attributable to their organisations. AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract The highest reaches of our planet experience some of the most extreme weather on Earth and hold very significant supplies of freshwater for communities downstream. However, we know very little of the meteorological detail about this high-altitude frontier. We address this here with new winter weather observations from the upper reaches of Mount Everest (2019–2021). We show that substantial sublimation rates are possible, with losses up to 2.5mm day−1. Wind chill plunging to −83°C and frostbite possible in less than one minute also attest to the severe cold stress facing mountaineers attempting winter ascents of this most iconic peak. Reaching up to 8849m above mean sea level (asl), the upper slopes of Mount Everest provide one of the most climatically extreme environments on the planet. It is also an environment likely to be warming rapidly, due to the tendency for temperature increases to amplify at greater elevations in the troposphere under climate change (Pepin et al., 2015). However, our understanding of high-altitude climates has been severely limited by an understandable lack of basic in situ observations (Matthews, 2020a). The logistical demands associated with climbing to such heights challenges the initial installation of automatic weather stations (AWSs). Those AWSs which do get deployed must then withstand extreme cold and severe winds if they are to remain operational. Mountain regions that are hardest to reach, at those times of year when the weather is most extreme, have therefore been monitored least, meaning winter conditions on the planet's highest peaks remain largely a mystery. Through necessity, insights have mostly been taken from relatively coarse reanalysis data (Moore and Semple, 2011; Matthews et al., 2021). Not only does this monitoring gap represent a technical blind spot in the understanding of our planet's climate envelope, but the practical impacts for society may also be substantial. For example, the mountains of the Hindu Kush Himalayan region store the largest mass of ice outside the poles (Bolch et al., 2012), and the ongoing retreat is a major concern for regional freshwater security (Immerzeel et al., 2020; Nie et al., 2021). The potential future losses inferred by modelling studies is, therefore, of much relevance for resource management and hazard planning (Huss and Hock, 2018). However, such outlooks are underpinned by an understanding of glacier–climate interactions that is biased towards more accessible regions and seasons in the same way the observations are. Temperature index models, for example, are widely used for glacier mass balance projections (Hock et al., 2019), but they appear unsuited to capture the important role of sublimation, which is amplified at higher altitudes (Stigter et al., 2018; Litt et al., 2019) and in the winter season (Wagnon et al., 2013). In this short article, we address the issues raised above by assessing the first observations from winter recorded by the one of the world's highest weather station networks on Mount Everest. We include the most-constrained estimate yet of air temperatures and cold stress on the summit, and we consider mechanisms of mass loss from the highest glacier on Earth. Data and methods We use meteorological data from the weather station network installed by the 2019 National Geographic and Rolex Everest Expedition, which is described by Matthews et al. (2020a,b) and summarised in Table 1. We summarise the weather across the full (>4500m) elevational range using simple descriptive statistics. For the summit – which sits ~400m above the Balcony AWS – we estimate air temperatures and cold stress using the temperature lapse rate and the assumption that wind speeds are the same as at the South Col AWS (which is situated in a less sheltered location than the Balcony). The temperature lapse rates were calculated as a linear regression of hourly temperature at the AWSs against elevation (cf. Immerzeel et al., 2014; Heynen et al., 2016), after first smoothing all temperatures with a running 24-hour mean to reduce the impact of solar heating which may affect measurements during brief spells of light winds (Matthews et al., 2020a,b). Table 1. Specifications of all the automatic weather stations installed across the slopes of Mount Everest. Phortse Base Camp Camp II South Col Balcony Latitude (°N) 27.8456 27.9952 27.9810 27.9719 27.9826 Longitude (°E) 86.7472 86.8406 86.9023 86.9295 86.9292 Elevation (m asl) 3810 5315 6464 7945 8430 Operation 24 April 2019 to present 10 October 2019 to present 9 June 2019 to present 22 May 2019–18 July 2021 23 May 2019– 1 January 2020 Abbreviation: m asl, metres above mean sea level. Wind chill temperature (WCT) and facial frostbite time (FFT) were then computed with the estimated summit air temperature and wind speed following Moore and Semple (2011): WCT = 13.12 + 0.621 T − 11.37 V 0.16 + 0.3965 T V 0.16 (1) FFT = − 24.5 0.667 V + 4.8 + 2111 − 4.8 − T − 1.688 (2)in which T is the air temperature (°C) and V is the wind speed (kmh−1). Strictly, the latter refers to a reference height of 10m above the surface, whereas the Everest weather stations' wind sensors are at approximately two metres above ground, a height at which wind speeds are likely slower due to friction with the land surface. Hence, this should lead to a high (warm) bias in WCT. However, the equation is also strictly valid for an air density between two and three times higher than at the summit of Everest, translating to a low (cold) bias in our application. Following Moore and Semple (2011), we assume that these biases approximately cancel. To explore point surface mass balance during the winter of the world's highest glacier (South Col Glacier, hereafter the SCG), we use data from the South Col AWS (7945m asl). This station was the highest of the network installed in 2019 that was equipped with all the sensors required to resolve the surface energy balance (SEB) and is separated from the SCG by a horizontal distance of only a few hundred metres. We use COSIPY (the COupled Snowpack and Ice surface energy and mass balance model in PYthon: Sauter & Schneider, 2015) to infer the SEB for the SCG, with mass input via snowfall estimated from the precipitation recorded at Phortse (3810m asl). We set all COSIPY model parameters (e.g. roughness lengths) as in Potocki et al. (2022). We fill brief periods of missing data at the South Col with ERA5 reanalysis data (Hersbach et al., 2020) downscaled to the AWS using machine learning (see Potocki et al., 2022 for details). The initial snow depth (0.94m) for the COSIPY simulation model was taken from the 1km2 Sentinel-1 snow depth product for 13 May 2019 (Lievens et al., 2019), which is 10 days prior to the installation of the AWS (and the beginning of the COSIPY model run). Given the highly variable topography, the accuracy of this Sentinel product for the SCG is questionable; however, it is the highest resolution snow depth product, and preliminary analysis (not shown) indicated low sensitivity to initial snow depth in the COSIPY simulations. Results Winter stands distinct from the other seasons, with air temperatures and insolation plunging to their lowest values and wind speeds reaching their peak (Figure 1). The highest (3s) gust recorded by the network (66ms−1; not shown) was indeed recorded at the South Col during January 2020 – immediately before the wind sensors went offline. The change to colder, conditions at the onset of winter generally magnifies with elevation, as the lapse rate steepens in this season meaning air temperatures fall more rapidly with height (Figures 1a and e). Relative humidity is noteworthy in winter because of its very high variability at synoptic timescales (Figure 1a). Although not quite as pronounced, the same high-frequency variance is evident in the air temperatures (Figure 1b). We attribute this wintertime peak in transitions between warmer, moister air masses and cooler, drier circulations, to the arrival of the Subtropical Jet over the Himalaya. The passage of waves in this westerly flow drives the variation in air mass character; and wind speeds peak when the jet axis crosses Everest (Matthews et al., 2020b). Figure 1Open in figure viewerPowerPoint Mean daily values of selected variables from the automatic weather stations (AWSs). (a) Relative humidity; blue line in the panel represents cumulative precipitation at Phortse, (b) daily temperature, (c) average wind speed, (d) incoming solar radiation and (e) lapse rates across all stations. The lapse rates were calculated as the slope coefficient from regressing running 24-h mean air temperature at the AWSs against their elevations. The dashed line in all the plots represents data filled from ERA5 reanalysis data at South Col. For all the panels, the blue shaded region represents the winter season (DJF). The elevation-enhanced drop in air temperature with the arrival of winter, combined with the amplification of winds, compound to generate extreme conditions of profound cold stress for the summit. The mean winter WCT is −61.2°C (Table 2), whilst the lowest daily mean observed during the period of observation was −77°C (on 31 December 2020) (Figure 2). The individual hour with the lowest WCT on the other hand reached −83°C, when the mean wind speed was 36ms−1 and the temperature was −48°C (in 19 January 2020). The FFT tells a similar story, indicating a mean exposure time in winter of less than two minutes before uncovered skin would be at high risk of frostbite (Table 2). Values much less than one minute in Figure 2 underscore the dangers of exposing skin for even the briefest of periods on Mount Everest during extreme winter cold events. We note that the monthly mean WCT and FFT shown in Figure 2 are in reasonably good agreement with the National Centers for Environmental Prediction (NCEP) reanalysis-based assessment by Moore and Semple (2011), but we find a less severe cold hazard during the monsoon. Table 2. Seasonal daily mean values estimated at the summit. The standard deviation values are represented by the plus/minus terms. Season T (°C) WS (ms−1) WCT (°C) FFT (min) Monsoon (JJAS) −18.8 ± 2.7 5.2 ± 2.6 −28.1 ± 4.8 9.8 ± 3.6 Post-monsoon (ON) −30.1 ± 4.7 13.9 ± 6.7 −48.4 ± 9.1 2.7 ± 2.1 Spring (MAM) −32.1 ± 5.7 9.6 ± 4.7 −48.5 ± 8.8 3 ± 1.6 Winter (DJF) −37.3 ± 4.4 18 ± 6.5 −61.2 ± 8.5 1.3 ± 0.8 Abbreviations: FFT, facial frostbite time; WCT, wind chill temperature; WS, wind speed JJAS: June, July, August, September; WS: Wind speed; MAM: March, April, May; DJF: December, January, February; ON: October, November Figure 2Open in figure viewerPowerPoint Boxplot of daily mean air temperature, wind chill temperatures (WCTs) and facial frostbite time (FFT) estimated at the summit. The FFT is plotted on the right y-axis. The horizontal line within the box denotes the median, whereas the first and third quartile are represented by the top and bottom of the box, respectively. The whiskers indicate the range (minimum and maximum values), and the points indicate values that are greater than 1.5 times the upper quartile and are often regarded as outliers. The blue shaded region represents winter season. Owing to the high levels of insolation, our COSIPY simulation suggests that the net shortwave radiation flux is the largest energy source for the SCG (Figure 3a). Most of this energy is then dispersed by the net longwave radiation, followed by the turbulent (sensible and latent) heat fluxes, and finally energy for melting. The model results indicate that some meltwater may be generated in the monsoon season, despite the air temperature staying well below 0°C. The majority of the ablation, however, is as sublimation (Figure 3), with cumulative losses of 539mm w.e. in between 23 May 2019 and 30 June 2021: more than nine times the amount of surface melt during the same period (60mm w.e.) (Figure 3b). Temporal variations in sublimation show that although the highest mean rate is in the post-monsoon (October–November), peak rates occur in the winter season, reaching up to 2.5mm w.e. day−1. To put such losses into context, integrating the total sublimation from the colder, windier, and drier months of the post-monsoon and winter (Figure 3d) through the ~150 days of that period indicates that 0.28m w.e. could be ablated during that time. If that figure is also representative of conditions at the summit, and if snow density there is in the region of 300–500kgm−3, then the peak could be expected to 'shrink' by up to 0.5–0.9m during this period (assuming no input of snow via precipitation or wind re-distribution). Given this potential for mass loss, and the considerable efforts expended in measuring the height of Mount Everest (Adhikari and Slater, 2020), we suggest that examining inter-annual variability in the mass balance of the summit could be an interesting direction for future research. Figure 3Open in figure viewerPowerPoint (a) Mean daily energy fluxes at South Col. (b) Cumulative surface melt and sublimation. The secondary axis represents the snow depth over the simulation period. (c) Temperature and wind speed (secondary axis) at South Col. (d) Bar plot of total sublimation in each season and boxplot of seasonal variation of daily sublimation rates in each season. The bar plot is plotted on the left y-axis, whereas the box plots are plotted against the right y-axis. The horizontal line within the box denotes the median, whereas the first and third quartile are represented by the top and bottom of the box, respectively. The whiskers indicate the range (minimum and maximum values), and the points indicate values that are greater than 1.5 times the upper quartile and are often regarded as outliers. From panels (a)–(c), the blue shaded region represents the winter season (DJF). Conclusions In this short article, we provided the first assessment of wintertime weather measured by in situ stations on Mount Everest. The assessment highlighted the extent of the severe cold hazard on the upper mountain in more detail than has previously been possible, identifying hourly wind chills as low as −83°C and daily means reaching −77°C. This cold hazard amplifies more with the onset of winter than at lower elevations because the lapse rate also steepens in this season. Our investigation also reveals that the combination of strong winds and low relative humidity during the wintertime is enough to drive significant sublimation (2.5mm w.e. day−1) on the upper reaches of Mount Everest. Quantifying mass loss from such previously unexplored seasons and locations is important to understand the impact of climate change on water resources in High Mountain Asia. In the case of Mount Everest, it has additional symbolic value, as it helps illuminate the extent to which the height of Earth's highest mountain is subject to change. Exploring this more could be an interesting avenue for future research. Conflict of interest statement The authors declare no conflicts of interest. References Adhikari A, Slater J. 2020, December 8. Its official: Mount Everest just got a little higher. The Washington Post. https://www.washingtonpost.com/world/asia_pacific/mount-everest-height-nepal-china/2020/12/08/a7b3ad1e-389a-11eb-aad9-8959227280c4_story.html. Bolch T, Kulkarni A, Kääb A et al. 2012. The state and fate of Himalayan glaciers. Science 336(6079): 310– 314. Heynen M, Miles E, Ragettli S et al. 2016. Air temperature variability in a high-elevation Himalayan catchment. Ann. Glaciol. 57(71): 212– 222. Hersbach H, Bell B, Berrisford P et al. 2020. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146(730): 1999– 2049. Hock R, Bliss A, Marzeion BEN et al. 2019. GlacierMIP – a model intercomparison of global-scale glacier mass-balance models and projections. J. Glaciol. 65(251): 453– 467. Huss M, Hock R. 2018. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Chang. 8(2): 135– 140. Immerzeel WW, Lutz AF, Andrade M et al. 2020. Importance and vulnerability of the world's water towers. Nature. 577(7790): 364– 369. Immerzeel WW, Petersen L, Ragettli S et al. 2014. The importance of observed gradients of air temperature and precipitation for modeling runoff from a glacierized watershed in the Nepalese Himalayas. Water Resour. Res. 50(3): 2212– 2226. Lievens H, Demuzere M, Marshall HP et al. 2019. Snow depth variability in the Northern Hemisphere mountains observed from space. Nat. Commun. 10(1): 4329. Litt M, Shea J, Wagnon P et al. 2019. Glacier ablation and temperature indexed melt models in the Nepalese Himalaya. Sci. Rep. 9(1): 5264. Matthews T, Perry LB, Koch I et al. 2020a. Going to extremes: installing the world's highest weather stations on Mount Everest. Bull. Am. Meteorol. Soc. 101(11): E1870– E1890. Matthews T, Perry LB, Koch I et al. 2021. Himalayan high: weather stations on Mount Everest reach new heights. Bull. Am. Meteorol. Soc. 102(5): 422– 428. Matthews T, Perry LB, Lane TP et al. 2020b. Into thick(er) air? Oxygen availability at humans' physiological frontier on Mount Everest. Iscience 23(12): 101718. Moore GWK, Semple JL. 2011. Freezing and frostbite on Mount Everest: new insights into wind chill and freezing times at extreme altitude. High Alt. Med. Biol. 12(3): 271– 275. Nie Y, Pritchard HD, Liu Q et al. 2021. Glacial change and hydrological implications in the Himalaya and Karakoram. Nat. Rev. Earth Environ. 2(2): 91– 106. Pepin N, Bradley RS, Diaz HF et al. 2015. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Chang. 5: 424– 430. Potocki M, Mayewski PA, Matthews T et al. 2022. Mt. Everest's highest glacier is a sentinel for accelerating ice loss. npj Clim. Atmos. Sci. 5(1): 7. Sauter T, Schneider C. 2015. COupled Snowpack and Ice surface energy and MAss balance model. Geosci. Model Dev. 47(3): gmd-8- 3911- 2015. Stigter EE, Litt M, Steiner JF et al. 2018. The importance of snow sublimation on a Himalayan glacier. Front. Earth Sci. 6: 108. Wagnon P, Vincent C, Arnaud Y et al. 2013. Seasonal and annual mass balances of Mera and Pokalde glaciers (Nepal Himalaya) since 2007. Cryosphere 7(6): 1769– 1786. Early ViewOnline Version of Record before inclusion in an issue FiguresReferencesRelatedInformation

<p>Recent glacier mass changes are very heterogeneous in High Mountain Asia, owing to climatic variability and the mass balance sensitivity to climate, which may differ from one region to another. Mera glacier in the Everest region is one of the longest field-based monitored and well-studied glaciers of the Central Himalaya. In this study, we examine the sensitivity of Mera glacier mass balance to climate variables using the COupled Snowpack and Ice surface energy and mass balance model in PYthon (COSIPY), using 4 years (2016-2020) of in-situ meteorological data recorded at different elevations in the ablation and accumulation zones of the glacier. This shows that the net short-wave radiation is the main energy input at the surface, and in turn albedo is a key parameter controlling the glacier mass balance. As a result, at 5360 m asl, in the ablation zone, surface melt accounts for 90% of mass loss whereas sublimation and subsurface melt account for less than 10%. This analysis is performed at point scale at 5360 and 5770 m asl, in the ablation and accumulation zones respectively, as well as in a distributed way. We produce and analyze 88 distinct climatic scenarios, varying from dry and warm to wet and cold conditions. Dry conditions, primary during the pre-monsoon and secondary during the monsoon, strongly decrease the glacier mass balance, revealing that the annual amount and the seasonal distribution of snowfalls primary drives the glacier-wide mass balance of Mera Glacier.   </p>

Mt. Everest is the highest mountain in the world, with an elevation ending at 8848.86 m above sea level, providing unique opportunity for direct observation of the upper troposphere. Utilizing the data from recently established five automatic weather stations (AWSs) network along the Everest climbing route, as part of the National Geographic and Rolex Perpetual Planet Expedition to Mount Everest 2019, from June 2019 to May 2020, this study investigates the meteorological environment over the southern slope of the Mt. Everest. Precipitation, temperature, radiations (income and outgoing short wave and long wave radiation), wind speed and direction along with derived variables like Lapse Rate, Precipitation Gradient, 6.11 hPa Isoline, and zero-degree Isotherm are analyzed with the aim of understanding altitudinal variation. Precipitation is mainly concentrated in monsoon with highest in Phortse (530 mm). Analysis of temperature lapse rate shows the highest lapse rate (-5.6 ℃ km-1) in monsoon and lowest in post-monsoon (-7℃ km-1). The precipitation analysis reveals that the vertical and horizontal precipitation gradient for monsoon is -63 mm km-1 and is -8.6 mm km-1 however, during the post-monsoon, precipitation increased by 0.75mm km-1 and 4.6 mm km-1, respectively. Similarly, westerly winds dominate during winter in upper station while it’s nearly uniform for lower stations. Radiation, likewise, are highly correlated between the stations, with incoming shortwave being the highest in the upper station, South-Col. Both isoline and isotherm lines are observed at around 6000 m above sea level. The one-year data has revealed some of the interesting pictures of high-altitude meteorology, but long-term data with fewer data gaps should be required to confirm these patterns.

Mount Everest, known locally as Sagarmatha or Qomolangma, is the world's highest (8849m), and arguably most iconic, peak. That allure draws large numbers of tourists to Nepal every year with hopes of seeing or climbing the famed mountain. Importantly, the large tourist presence has wide ranging environmental (Napper et al., 2020; Aubriot et al., 2019; Semple et al., 2016; Faulon and Sacareau, 2020; Miner et al., 2021; Byers, 2005), cultural (Rai, 2017; Nepal et al., 2020), societal (Pallathadka, 2020; MOFA, 2021) and economic (Nyaupane, 2015; Mu, 2019) implications for the Khumbu Region of Nepal. Using data from a new array of automatic weather stations (AWSs) installed as part of the 2019 National Geographic and Rolex Perpetual Planet Everest Expedition (Matthews et al., 2020a,b) shows that seasonal variations in the weather on Mt. Everest modulates the timing of optimum climbing conditions for mountaineers. However, the influence of seasonality on the likelihood of visitors' ability to view the famed summit from Mt. Everest's (Nepalese) Base Camp has not been assessed. Here, we utilize previously unpublished photos taken twice-daily by an automatic camera at the Base Camp AWS (Figure 1), alongside meteorological data, to examine the impacts of weather on the visibility of this iconic peak that draws visitors from all over the world. Using the seasonal timing identified by Matthews et al. (2020a), we investigate the conditions of both the 2019 and 2020 post-monsoon seasons (1 October to 30 November) using hourly measurements from five AWSs, at varying altitudes, along the Mt. Everest summit route (Figure 2a): Phortse (3810m), Everest Base Camp (5315m), Camp II (6464m), South Col (7945m) and Balcony (8430m). Here, we examine data encompassing two post-monsoonal seasons, spanning 1 August 2019 to 31 January 2021, the time periods when the upper slopes of Mt. Everest are least obscured by clouds. In addition to the numerical data recorded by the AWSs, a Campbell Scientific Canada CCFC Field Camera looking eastward toward the summit of Mt. Everest was also installed at Everest Base Camp during the 2019 Everest Expedition (Figure 1). As shown in Figure 2(a), the camera's viewshed from left to right (north to south, respectively) primarily shows Mt. Everest's West Ridge (~7000–7200m), Mt. Everest's summit (8849m) and Nuptse's sub-peak (~7400m). The camera takes two photographs daily at 0937 Nepal time (NPT) (0352 utc) and at 1437 NPT (0852 utc), respectively. According to meteorological data, the summer monsoon brings significant changes to the Nepal Himalaya (Khadka et al., 2021), and herein, we show that the monsoon's departure brings equally significant changes once more. One noticeable difference is the absence of cloud cover during the post-monsoon season, as suggested by AWS measurements of incoming shortwave radiation and downward longwave radiation measured by the Camp II AWS (Figures 3a,b). Using the mean daily values for incoming shortwave and downward longwave radiation with a three-day running mean (Figures 3a,b), we can assess trends across seasonal changes for both 2019 and 2020. The transition from the monsoon to post-monsoon season shows an increase in incoming shortwave radiation accompanied by a decrease in downward longwave radiation. In support of the meteorological data, images collected from Base Camp looking towards the summit, which first became available on 11 October 2019, confirm an absence of post-monsoonal cloud cover. Figures 4 and 5 each contain five 32-day photo arrays depicting the seasonal changes in morning and afternoon cloud cover, respectively. Through manual assessment, these arrays show that the clouds enshrouding Mt. Everest during the monsoon season are replaced with clearer skies as the post-monsoon progresses. Of note, cloud coverage tends to increase into the afternoon when compared with the morning, especially during the monsoon season as shown by the differences between Figures 4(e) and 5(e). During the post-monsoon, afternoons also tend to be cloudier than mornings, but the diurnal variation is less pronounced than during the monsoon (Figures 4c and f; 5c and f). More photographic data from future years are needed to confirm this. Depending on the season, trekkers and climbers may want to be in line of sight of the peak earlier in the day should their goal be to see Mt. Everest's summit. We also monitored the changes in relative humidity (RH) at the Phortse, Base Camp, Camp II, South Col and Balcony AWSs for 2019 and the lower four stations for 2020 using the mean daily RH with a three-day running mean shown in Figures 3(e) and (f). The change in season from the monsoon to post-monsoon is accompanied by a significant drop in RH (Figures 3e and f) as the regional winds shift direction from off the Bay of Bengal and the Arabian Sea to more westerly continental trajectories (Perry et al., 2020). The drop in RH is most noticeable for the higher stations of Camp II and South Col located at 6464 and 7945m asl, respectively. In the 2019 post-monsoon, these stations experience a decrease from an average of around 90% RH to 20% RH over the course of 15 days, with a similar decrease from 80% to 20% observed in 2020, staying below 50% for the majority of the season. While data at the South Col station are missing during the transition to the post-monsoon, the RH is initially close to that at Camp II, so we expect it to have similar values up to and through the monsoon / post-monsoon transition, after which it begins to diverge slightly. Lower humidity post-monsoon is consistent with a decrease in cloud cover at that time. Another quantity of interest is the specific humidity calculated for each station in both years with a three-day running mean (Figures 3g,h). Of the five stations, the Phortse and Base Camp AWSs see the largest decreases in specific humidity over a short period, from 8.8 to 4.0 and 6.1 to 2.0gkg−1, respectively, occurring at the same time as the similarly large decrease in RH. For both years, the specific humidity approaches its minimum towards the end of the post-monsoon for all stations. The post-monsoon is also accompanied by a significant decrease in precipitation at Phortse (the only AWS for which precipitation data are currently available), shown by the plateauing of cumulative precipitation at the start of the season (Figures 3i,j). While a lack of precipitation at lower elevations alone may not directly indicate a lack of cloud coverage near the summit, the clear conditions along the lower section of the Nepali route will benefit climbers looking to catch an early glimpse of Mt. Everest. Compared to the transition from the monsoon to the post-monsoon, the continuation into winter currently provides much smaller but still noteworthy changes in atmospheric conditions. RH shown in Figures 3(e) and (f) continues to decrease and reaches a minimum for the observed time period. The mean emissivity in Figures 3(c) and (d) sees little change, falling from 0.68 (±0.11) to 0.67 (±0.14) for 2019, and for 2020, from 0.62 (±0.07) to 0.61 (±0.09). Specific humidity also remains fairly consistent near the minimum at each station throughout the two-month winter period observed for both years, except for a small peak in 2021 at the beginning of January. Despite the photo arrays in Figures 4 and 5 showing similar cloud coverage to the post-monsoon period, the Phortse AWS recorded more precipitation, especially in 2019, as shown in Figures 3(i) and (j). We also want to emphasize our limited period of records and that we cannot rule out potential anomalous conditions during this season as big storms limiting visibility can occur during any month under favourable synoptic circulation. Tropical cyclones can also impact the region during the post-monsoon season, as evidenced by the devastating Cyclone Hudhud in October 2014 in the Annapurna Region (Simon Wang et al., 2015). Taken altogether, our meteorological and photographic evidence suggests a relatively cloud-free post-monsoon period on Mt. Everest over the past 2 years, particularly in 2020 with the mean thermal emissivity remaining close to the average of the season for the majority of the period. The resulting high visibility period that we identify herein has large implications for tourism and for the residents of the region. Tourism has been increasing rapidly in Nepal; from 45 970 visitors in 1970 (Neupane et al., 2012) to 1 197 000 visitors bringing Nepal US$714 million in revenue in 2019 (World Bank, 2021a,b), the last year of available data prior to the COVID-19 pandemic, which significantly decreased global tourism (Weissenbach, 2021). As of 2019, 16.5% of tourists to Nepal declared their travel purpose as ‘trekking and mountaineering’ (MOCTCA, 2020). This explains why the increase in overall tourism is accompanied by a similarly rapid increase in the number of tourists visiting Sagarmatha National Park, which surrounds Mt. Everest's southern flanks, from 3600 visitors in 1979 (UNESCO, 2021) to over 35 000 in 2013 (Baral et al., 2017) and 57 289 in 2019, with almost 43% visiting around the post-monsoon (MOCTCA, 2020). The influx of visitors not only provides much needed financial resources to the Khumbu Region, but also creates stress on the local environment (Byers, 2005; Semple et al., 2016; Aubriot et al., 2019; Faulon and Sacareau, 2020; Napper et al., 2020; Miner et al., 2021). While most climbers attempt to summit in the pre-monsoon season (April/May/June) and 32% of visitors also travel there during this time (MOCTCA, 2020), using meteorological and photographic data, we determine that the post-monsoon season (October/November) offers a markedly better opportunity for trekkers on the Nepali side of the mountain wanting to see – rather than climb – Mt. Everest's summit during this secondary climbing window. Due to the significantly higher burden of supporting staff and resources required by prospective summit climbers, the pressure of the mountain's workers and the environment during the pre-monsoon climbing season could be significantly reduced if even more trekkers who are seeking to view Mt. Everest, rather than summit, travelled there during the post-monsoon. This research was conducted in partnership with National Geographic Society, Rolex and Tribhuvan University, with approval from all relevant agencies of the Government of Nepal. We gratefully acknowledge the communities of the Khumbu Region and the Sherpa climbing team for their efforts in aiding the setup and maintenance of the AWSs, as this work would not be possible without them. We also thank the reviewers for their time and their helpful feedback which improved this manuscript.

Abstract Nepal is highly vulnerable to climate change with increased fire occurrences and fire burned areas in recent years; therefore, we accessed the climatic drivers for its variability using fire burned areas product of Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 and 2020. The peak fire burned areas were observed in the spring season (~91%) from March to May, especially higher in the lowlands of the western and central parts. At the interannual timescale, low precipitation, humidity, soil moisture, and high temperature supported the existence of spring fire. Combining these factors induces drought conditions, enhancing evapotranspiration from vegetation and providing more combustible fuels. Furthermore, the El Niño phase in the central‐eastern Pacific Ocean is related to the weakened westerly moisture transport and moisture divergence that creates dry and warm conditions leading to increased fire activities. Thus, this study could be helpful for preparedness, management, and policy‐making to limit the multi‐dimensional losses in the ecosystem and society due to fire.

The predictability of the weather on Mount Everest's upper slopes can be a matter of life or death for those trying to climb the world's highest mountain, yet the performance of forecasts has been almost unknown due to a lack of surface observations. The extent to which climate change may be affecting this iconic location is also uncertain for the same reason. To address this data limitation, the National Geographic and Rolex Perpetual Planet Expedition installed the world's highest weather station network (reaching within 420 m of the summit) on the Nepal side of Mount Everest in 2019. Its observations have already generated considerable advances in understanding the meteorological environment on the mountain's upper slopes, but the network was compromised by damage to the highest stations in recent years. Here, we describe the expedition that upgraded the network and took it to new heights, focusing on the installation at the Bishop Rock (8,810 m MSL), just below the summit. Almost 70 years after Everest was first climbed successfully, we can now provide open access data to illuminate conditions at Earth's highest climate frontier.

We present a multisite evaluation of meteorological variables in the Everest region (Nepal) from ERA5-Land and High Asian Refined Analysis, version 2 (HARv2), reanalyses in comparison with in situ observations, using classical statistical metrics. Observation data have been collected since 2010 by seven meteorological stations located on or off glacier between 4260 and 6352 m MSL in the upper Dudh Koshi basin; 2-m air temperature, specific and relative humidities, wind speed, incoming shortwave and longwave radiations, and precipitation are considered successively. Overall, both gridded datasets are able to resolve the mesoscale atmospheric processes, with a slightly better performance for HARv2 than that for ERA5-Land, especially for wind speed. Because of the complex topography, they fail to reproduce local- to microscale processes captured at individual meteorological stations, especially for variables that have a large spatial variability such as precipitation or wind speed. Air temperature is the variable that is best captured by reanalyses, as long as an appropriate elevational gradient of air temperature above ground, spatiotemporally variable and preferentially assessed by local observations, is used to extrapolate it vertically. A cold bias is still observed but attenuated over clean-ice glaciers. The atmospheric water content is well represented by both gridded datasets even though we observe a small humid bias, slightly more important for ERA5-Land than for HARv2, and a spectacular overestimation of precipitation during the monsoon. The agreement between reanalyzed and observed shortwave and longwave incoming radiations depends on the elevation difference between the station site and the reanalysis grid cell. The seasonality of wind speed is only captured by HARv2. The two gridded datasets ERA5-Land and HARv2 are applicable for glacier mass and energy balance studies, as long as either statistical or dynamical downscaling techniques are used to resolve the scale mismatch between coarse mesoscale grids and fine-scale grids or individual sites.

We find that the historic first wintertime ascent of K2 in January 2021 by a Nepalese team was aided by weather that was anomalously favourable. An upper-level ridge brought low wind speeds, and relatively high temperatures and air pressures that were quite uncharacteristic of winter. Extraordinary ability in the climbing team aligning with opportune weather therefore explains how this last great challenge in extreme-altitude mountaineering was overcome.

Records from new high altitude weather stations reveal the meteorological conditions on Mt Everest during the 2019 monsoon. Using data from June-October, we show that the temperature, humidity, and winds announce the arrival of the monsoon, with changes that amplify with elevation. The largest change is therefore at the summit, where we estimate that monthly mean air temperature increased by 5.5 °C between June and July to average -19.1 °C over the monsoon. Such warming takes temperatures into the realm of winter conditions on much lower mountains of the mid-latitudes, illustrated with the well-known Mount Washington observatory (1,916 m; New Hampshire, USA). Although other dangers of climbing Everest may be enhanced during the monsoon, the cold induced hazard is much reduced.

AbstractThe 2007–19 glaciological mass-balance series of Mera Glacier in the Everest Region, East Nepal, is reanalysed using the geodetic mass balance assessed by differencing two DEMs obtained from Pléiades stereo-images acquired in November 2012 and in October 2018. The glaciological glacier-wide annual mass balance of Mera Glacier has to be systematically decreased by 0.11 m w.e. a−1to match the geodetic mass balance. We attribute part of the positive bias of the glaciological mass balance to an over-estimation of the accumulation above 5520 m a.s.l., likely due to a measurement network unable to capture its spatial variability. Over the period 2007–19, Mera Glacier has lost mass at a rate of −0.41 ± 0.20 m w.e. a−1, in general agreement with regional averages for the central Himalaya. We observe a succession of negative mass-balance years since 2013.