The study’s central insight is that in high-latitude countries, the standard metric used to estimate solar potential, known as photovoltaic power generation potential, is fundamentally incomplete. That metric typically considers only solar irradiance and ambient air temperature. But in Sweden, where fixed-tilt, non-tracking panels dominate the market and snow can blanket arrays for weeks, two additional factors matter enormously: the tilt angle at which panels are installed and the accumulation, melting and sliding of snow on the module surface. Snow alone is estimated to strip roughly 15 percent from annual photovoltaic generation in Stockholm. Ignoring it, the authors argue, produces systematically misleading forecasts for cold regions.

To build a more realistic picture, the team applied a validated photovoltaic installation model that runs at an hourly timestep and incorporates air temperature, solar radiation and a full snow model covering snowfall, accumulation, covering, sliding and melting. The model calculates snow albedo as a function of snow depth, distinguishes dry winter snow from wetter spring conditions, and applies a sliding factor that lets partially cleared panels keep generating as snow slips off tilted surfaces. Melting rates differ between winter and spring, reflecting empirical values for Swedish urban conditions. The model was validated against measured data from a real rooftop plant, giving the projections a grounding that purely theoretical estimates often lack.

The researchers ran their model for four cities chosen to span Sweden’s climatic range: Malmö, Gothenburg, Stockholm and Kiruna, the last of which sits well above the Arctic Circle. Climate inputs came from the HCLIM regional climate model, part of the EURO-CORDEX downscaling of CMIP6 global simulations, run at 12.5 kilometer resolution and driven by three different global climate models: CNRM-ESM2-1, MPI-ESM1-2-HR and MIROC6. Two Shared Socioeconomic Pathways bracketed the future: SSP1-2.6, a lower-forcing sustainability scenario, and SSP3-7.0, a higher-forcing pathway with limited mitigation. The historical reference period ran from 1951 to 2014, with projections extending to 2100.

The projected climates diverge sharply by scenario. Under SSP3-7.0, annual global horizontal irradiance declines by around 30 to 40 watts per square meter in all four cities, while under SSP1-2.6 it remains essentially stable. Air temperatures in the three southern cities rise from roughly 4 to 9 degrees Celsius historically to 6 to 13 degrees by 2100, with warming trends of about 0.33 to 0.38 degrees per decade under the high-emission scenario. Kiruna warms fastest of all, at up to 0.51 degrees per decade, reaching an increase of approximately 4.42 degrees by century’s end. Snowfall declines significantly everywhere, with southern Sweden expected to retain only about two-thirds of historical snowfall under SSP1-2.6 and a nearly negligible amount under SSP3-7.0. Kiruna, remarkably, keeps about 90 percent of its snow, roughly 300 millimeters a year.

Against this backdrop, the photovoltaic results are striking. With panels installed at their calculated optimal tilt angles, annual generation is expected to be about 5 percent higher in the future than in the historical period, equivalent to roughly 33 kilowatt-hours per kilowatt-peak, under both scenarios. The gain is concentrated in spring, when reduced snow cover allows panels to exploit what is already one of the sunnier stretches of the Swedish year. This is precisely where the new study parts company with earlier work: previous assessments that neglected snow processes generally projected declines, because they saw only the falling irradiance and the efficiency penalty of warmer modules. Once snow is modeled explicitly, the loss of snow-related downtime outweighs both.

The interplay between variables is subtle. Correlation analysis using daily values shows that photovoltaic generation is positively associated with irradiance and air temperature but negatively with snowfall, with irradiance the dominant factor overall. Yet temperature’s role is double-edged: warmer cells convert sunlight less efficiently, but warmer air melts and slides snow off panels. The positive snow-melting effect only operates when two conditions coincide, namely no active snowfall and air temperatures above freezing. Below zero, extra warmth does nothing to clear the panels. In the milder south, where snow losses shrink, temperature’s negative effect on module efficiency begins to dominate, and the net temperature effect becomes small. In the snowier north, the melting effect remains significant.

A sensitivity analysis made the point vividly. By pairing radiation from one scenario with snow-and-temperature conditions from the other, the researchers showed that swapping the snow-and-temperature inputs alone could flip the sign of the generation trend, turning an increasing trajectory into a declining one or vice versa. In several city-model combinations, the combined effect of snowfall and air temperature matched or exceeded that of solar radiation itself. No single meteorological driver dominates universally across all locations and simulations, which is why the authors stress that multi-model assessments are essential for reliable projections in snow-affected regions.

The study also delivers practical engineering guidance. The median optimal tilt angle across all four cities and scenarios is approximately 35 degrees, and installing panels at these city-specific optima rather than the 15-degree angle most common in the current Swedish market would raise annual generation by 3 to 6 percent, or about 20 to 40 kilowatt-hours per kilowatt-peak. Under a recent change in Sweden’s building-permit framework, rooftop owners now have more freedom to choose their panel tilt, making this optimization immediately actionable. Future output is also projected to be less variable year to year than in the historical record, and the number of days with zero generation, along with the longest consecutive zero-output streaks, generally declines, in southern cities potentially halving under SSP3-7.0. That could ease the storage and backup burden of integrating solar into the grid.

There are caveats, and the authors are candid about them. Only four cities were analyzed, which may not capture the full regional variability of a country spanning wide latitudes at 12.5 kilometer model resolution. The snow model is deliberately simple, omitting the effects of precipitation on melting, and only three regional simulations were used, a small subset of the available ensemble, leaving room for systematic model biases. Prolonged zero-generation periods of one to two consecutive weeks remain possible in some years, and individual extreme years can still produce unusually high intermittency, with some high-emission projections for Stockholm exceeding the historical range. Kiruna’s polar nights, 22 days without sun, remain an irreducible constraint. Still, with Swedish photovoltaic capacity growing at more than 55 percent annually and national targets calling for a fully renewable power sector by 2040, the message for planners, investors and grid operators is clear: in the cold latitudes, the future of solar depends as much on what falls from the sky and sticks to the panel as on what shines on it.

Subject of Research: Climate change impacts on snow-affected photovoltaic power generation in high-latitude Sweden through 2100

Article Title: Impact of climate change on the photovoltaic power potential in Sweden across 21st century

Article References: Ruan, T., Wang, F., Laumert, B., Aldama-Campino, A., & Wang, W. (2026). Impact of climate change on the photovoltaic power potential in Sweden across 21st century. Communications Earth & Environment, 7(1), Article 822. https://doi.org/10.1038/s43247-026-04091-w

Image Credits: AI Generated

DOI: 10.1038/s43247-026-04091-w

Keywords: photovoltaics, solar energy, climate change, Sweden, snow cover, EURO-CORDEX, CMIP6, SSP scenarios, tilt angle optimization, renewable energy, high-latitude climate, energy modeling

The research, led by Kaifeng Ma of the Northwest Institute of Eco-Environment and Resources of the Chinese Academy of Sciences together with Junfeng Liu, Chuntan Han and Rensheng Chen, systematically evaluated six typical gridded evapotranspiration products against ground observations from ten sites spread across the high-latitude cold regions of the Northern Hemisphere. The products tested represent the main families of approaches currently in use: EB, a surface energy balance method; CAMELE, a collocation-analyzed multi-source ensemble; ETMonitor, a model driven by multi-source satellite observations; GLDAS, a land data assimilation system; GLEAM, a global land evaporation and soil moisture dataset; and ML, a machine learning based product. Each of these datasets embodies a different philosophy for estimating a flux that cannot be directly measured from space, and each has been validated extensively in temperate and tropical environments. What the team wanted to know was whether that validation record carries over to landscapes where subfreezing temperatures and persistent snow cover dominate for much of the year.

The answer, in short, is no. During the warm season, when soils thaw, vegetation is active and the physics of evaporation resembles the conditions under which most of these products were developed, the picture is mixed but tolerable. The machine learning product and CAMELE performed relatively well, achieving mean correlation coefficients of 0.72 and 0.60 respectively with the ground-based observations. Yet even in the warm season the study found significant spatial heterogeneity among sites, meaning that a product that tracks evapotranspiration skillfully at one location may drift badly at another only a few hundred kilometers away. That kind of spatial inconsistency matters enormously for applications such as basin-scale water balance calculations, where errors at individual grid cells propagate into regional water resource assessments.

The cold season is where the datasets truly fall apart. When snow blankets the ground, soils freeze and the sun sits low on the horizon, the reliability of all six products decreases substantially, and at most sites the correlation coefficient approaches zero. In practical terms, a correlation of zero means the product’s estimates of evapotranspiration carry essentially no information about what is actually happening on the ground. This is a striking result, because winter is precisely the period when the high-latitude water budget is subtle and easily misrepresented. Snow sublimation, the direct conversion of snow and ice into vapor, can remove a meaningful share of the winter snowpack, and blowing snow events can export moisture from a basin entirely. If gridded products cannot reproduce even the timing of these fluxes, then any attempt to close the regional water balance using them alone is built on sand.

To go beyond simple correlation-based evaluation, the team applied a statistical technique known as the three-cornered hat method, or TCH. Originally developed in metrology to estimate the individual instabilities of clocks when no absolute reference is available, the three-cornered hat approach exploits the differences between multiple imperfect datasets to estimate the relative uncertainty of each one without requiring a perfect ground truth. This is an elegant solution to a chronic problem in flux validation: eddy-covariance towers, the standard instruments for measuring evapotranspiration on the ground, are themselves imperfect, particularly in winter, when energy balance closure problems and instrument icing are well documented. By treating the products and the observations as a set of mutually correlated estimates, the TCH analysis allowed the researchers to quantify how much uncertainty each product contributes independently of the others.

The TCH results reinforced the seasonal pattern. Relative uncertainty was lower during the warm season than during the cold season across the product suite, confirming that the winter degradation is not an artifact of one flawed dataset but a systemic weakness shared by energy balance models, satellite-driven products, reanalysis systems, ensembles and machine learning approaches alike. The convergence of evidence from two independent evaluation frameworks, direct comparison with site observations and the model-free uncertainty decomposition, makes the conclusion difficult to dismiss. Whatever the technical lineage of a given product, the combined effects of low temperatures and snow cover push it beyond the regime in which its underlying assumptions were calibrated.

Why should the cold season be so hostile to evapotranspiration estimation? The technical reasons are rooted in process parameterization. Most evapotranspiration algorithms were built around the physics of liquid water: stomatal conductance of leaves, aerodynamic transfer from moist soil, and radiative driving by abundant sunlight. Under snow cover, the relevant processes shift to sublimation from a snow surface, vapor transport through a turbulent boundary layer over ice, and energy exchanges complicated by the high albedo and low thermal conductivity of snow. Field studies have long shown that closing the surface energy balance over homogeneous midwinter snowpack is notoriously difficult even with careful instrumentation, so it is unsurprising that global products struggle. In addition, the machine learning products, however skillful in the training domain, inherit the distributional blind spots of their training data, which are dominated by flux tower sites in warmer, snow-free conditions. When asked to extrapolate to a frozen tundra or taiga landscape in January, they are effectively guessing.

The stakes are rising because the high latitudes are warming faster than almost anywhere else on Earth. The Arctic has warmed nearly four times faster than the globe since 1979, and this amplified warming is reshaping snow cover duration, permafrost extent, vegetation composition and the partitioning of energy at the surface. Boreal peatlands are contributing an increasing share of regional evapotranspiration as the climate warms, and changes in winter sublimation feed directly into snow mass balance and spring runoff, which in turn supply major river systems. Climate models and hydrological projections rely on gridded evapotranspiration datasets both as inputs and as benchmarks. If those datasets are unreliable in the very regions undergoing the most rapid change, then model evaluations, drought indices and carbon budget assessments for the circumpolar North carry hidden errors that no amount of averaging will remove.

The authors are explicit about the practical implication: current mainstream evapotranspiration products should be used with caution in high-latitude cold regions, especially during the cold season. This is not a call to abandon the datasets, which remain valuable tools across most of the planet, but a warning against uncritical application at the climatic margins. The study points toward a clear research agenda: improving process parameterization under low-temperature and snow-covered conditions, expanding the network of winter flux observations in cold regions so that training and validation data reflect the frozen world, and developing products that explicitly represent sublimation and snow-atmosphere exchange rather than treating winter as a low-flux afterthought. Emerging techniques such as structure-from-motion photogrammetry for measuring snow surface sublimation offer promising routes to the ground truth data that winter validation has lacked.

For the broader Earth science community, the study is a reminder that global datasets are only as good as the environments in which they have been tested. The elegant statistics of ensemble products and deep learning can conceal, rather than cure, physical blind spots. As the planet’s frozen margins thaw and shift, the demand for trustworthy evapotranspiration information in the high-latitude Northern Hemisphere will only grow, and closing the gap between what the grids say and what the snow actually does has become one of the more urgent tasks in land surface science.

Subject of Research: Uncertainty in gridded evapotranspiration products across high-latitude cold regions of the Northern Hemisphere

Article Title: Substantial uncertainties in gridded evapotranspiration products in high-latitude cold regions of the Northern Hemisphere

Article References: Ma, K., Liu, J., Han, C., & Chen, R. (2026). Substantial uncertainties in gridded evapotranspiration products in high-latitude cold regions of the Northern Hemisphere. Theoretical and Applied Climatology, 157(10), Article 642. https://doi.org/10.1007/s00704-026-06549-5

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06549-5

Keywords: evapotranspiration, high-latitude cold regions, snow cover, three-cornered hat method, gridded datasets, remote sensing, machine learning, sublimation, climate change, Northern Hemisphere, land-atmosphere exchange, Theoretical and Applied Climatology

The observational record is striking. Drawing on measurements from 150 meteorological stations operated by the National Meteorological Information Center of the China Meteorological Administration, the team found that annual mean near-surface wind speed over the Tibetan Plateau declined at a rate of 0.159 meters per second per decade between 1979 and 2014. The decline was not spread evenly through the year. Spring showed the steepest weakening, with wind speeds falling by 0.235 meters per second per decade, a seasonal signal that matters because spring winds play a central role in the plateau’s exchange of heat and moisture with the surrounding atmosphere and in shaping the Asian monsoon system downstream.

To test whether climate models could reproduce this stilling, the researchers turned to the Coupled Model Intercomparison Project Phase 6, specifically its High-Resolution Model Intercomparison Project, known as HighResMIP. This experiment was designed in part to answer a long-standing question in climate science: would simply cranking up the horizontal resolution of global models improve their simulations? The team assembled 22 historical simulations from 11 model pairs, each pair consisting of a higher-resolution and a lower-resolution version of the same model, and compared them against the station observations over the 1979 to 2014 period.

The verdict on resolution was sobering. Both the higher-resolution and lower-resolution ensemble means captured the broad spatial pattern of wind speed across the plateau, but both badly underestimated the magnitude of the decline. The higher-resolution ensemble produced an annual trend of only 0.024 meters per second per decade, while the lower-resolution ensemble managed just 0.020 meters per second per decade. In other words, the models reproduced only about one-seventh of the observed stilling. Perhaps more telling, the difference between the two resolution tiers was small, suggesting that increasing horizontal resolution alone offers limited improvement in capturing the historical wind decline over this complex, high-altitude terrain.

This widespread stilling bias is not merely an academic curiosity. Near-surface wind speed governs a host of processes with real-world consequences: the evaporation of water from land surfaces, the dispersal of air pollutants, the mixing of the atmospheric boundary layer, and the output of wind turbines. The Tibetan Plateau is also the source region of major Asian rivers and a critical driver of monsoon circulation, so systematic errors in simulating its winds can propagate into broader failures in regional climate prediction. Previous studies have linked wind declines across China to factors ranging from urbanization and increased surface roughness to weakening pressure gradients and changes in large-scale circulation, but representing these processes faithfully in global models has remained elusive.

To dig into why the models fall short, the team applied a random forest machine learning analysis, a technique that can rank the relative importance of many candidate variables in explaining an outcome. The analysis pointed to biases in key dynamical and thermodynamical processes, with snow variability and surface sensible heat flux emerging as particularly influential factors. This makes physical sense. Snow cover on the plateau alters surface albedo and the partitioning of energy between sensible and latent heat, which in turn modulates the temperature contrasts that drive near-surface winds. If a model misrepresents how snow accumulates and melts across the plateau’s rugged topography, the resulting errors in surface heating can distort the very circulation features that generate wind.

Having identified which simulations performed best against observations using multiple statistical metrics, the researchers selected seven optimal models and constructed an optimal ensemble mean for future projections. Under the high-emission Shared Socioeconomic Pathway 5-8.5, the projections indicate that the stilling of the Tibetan Plateau will continue through the coming decades. Between 2015 and 2049, the ensemble projects an annual wind speed decline of approximately 0.02 meters per second per decade, unfolding alongside continued regional warming. While the projected rate is gentler than the historical observed trend, the direction of change is consistent, lending robustness to the conclusion that winds over the plateau will keep weakening in a warming world.

The juxtaposition of a robust future decline with a poorly simulated past decline creates something of a paradox for climate scientists. If models underestimate how much wind has already slowed, should we trust their projections of future slowing? The authors argue that the consistency of the projected decline across the selected optimal models, combined with the physical plausibility of continued warming-driven circulation changes, supports confidence in the sign of the projection even if the magnitude remains uncertain. At the same time, the study makes clear that closing the gap between simulated and observed trends requires more than computational horsepower.

That message may be the study’s most consequential takeaway for the modeling community. HighResMIP was conceived on the premise that finer grids would better resolve steep orography, coastlines, and mesoscale processes, and for some variables that promise has held. But for near-surface wind over the Tibetan Plateau, the near-identical performance of high- and low-resolution model pairs indicates that the bottleneck lies elsewhere, in the representation of physical processes such as snow-atmosphere interactions, surface energy budgets, and boundary-layer dynamics. Improving these process representations, the study suggests, may matter more than simply increasing spatial resolution for reducing persistent model biases in wind simulation.

For the people and ecosystems of the plateau, the implications extend well beyond model evaluation. Weakening winds influence glacier mass balance through altered turbulent heat exchange, affect the timing and intensity of the monsoon onset that billions of people depend on, and reshape the viability of wind energy development in one of the world’s most promising but fragile high-altitude environments. As the Third Pole continues to warm faster than the global average, the slow, steady quieting of its winds serves as another reminder that climate change reshapes even the most fundamental features of the atmosphere, and that our models, for all their sophistication, are still learning to listen.

Subject of Research: Evaluation and projection of near-surface wind speed trends over the Tibetan Plateau using CMIP6 HighResMIP simulations and station observations

Article Title: Near-surface wind speed over the Tibetan Plateau: evaluation, trend and projection

Article References: You, Q., Wu, F., Wu, T., Cai, Z., Sun, G., Jin, Z., Jiang, Z., Ullah, S., & Li, M. (2026). Near-surface wind speed over the Tibetan Plateau: evaluation, trend and projection. Climate Dynamics, 64(10), Article 411. https://doi.org/10.1007/s00382-026-08342-6

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08342-6

Keywords: Tibetan Plateau, near-surface wind speed, global stilling, CMIP6, HighResMIP, climate modeling, Climate Dynamics, SSP5-8.5, random forest, snow cover, sensible heat flux, monsoon

Alaska occupies a special place in the climate system. Its air temperatures are rising at roughly twice the pace of the contiguous United States, a hallmark of what scientists call polar amplification, the tendency of high latitudes to warm faster than the rest of the planet as melting snow and ice expose darker surfaces that absorb more sunlight and as atmospheric and oceanic circulation patterns shift heat poleward. What the new research demonstrates is that this amplification is not confined to the air. Soil temperatures in the permafrost region are rising too, and while they lag behind the pace of atmospheric warming, they are climbing at a rate that has surprised even the researchers who documented it.

Lead author Erin Oliver, a post-doctoral research associate in Washington State University’s Department of Crop and Soil Science who is based in Alaska, explains that the relationship between air and soil temperature is anything but straightforward in the far north. In more temperate latitudes, one would expect soils and air to warm at roughly the same rate, because the atmosphere directly heats the ground. But in Alaska, soils are buried under snow for more than half the year, and that snow blanket acts as an insulating layer, mediating how much of the atmospheric signal ever reaches the ground. Understanding how this seasonal insulation shapes regional differences in soil warming was a central motivation of the study.

The answer, it turns out, varies sharply across the state. Oliver and her co-author Claire Phillips, a research soil scientist at Washington State University, divided Alaska into three zones: one underlain by continuous permafrost, one with only scattered areas of permafrost, and one with no permafrost at all. In the two southern categories, soil temperatures rose at roughly half the rate observed in the permafrost region, a pattern the researchers attribute largely to snow cover. Snow tends to be deeper at lower latitudes, and a thicker winter blanket buffers the ground against swings in air temperature, slowing the pace of soil warming. In the high-latitude permafrost region, shallower snow offers less protection, and the ground absorbs more of the atmospheric trend.

The numbers are striking. Over the 27-year record, air temperatures in the permafrost region rose at a rate of 1.15 degrees Celsius per decade, while soil temperatures there climbed at 0.64 degrees Celsius per decade. In the rest of the state, soil warming proceeded at roughly half the permafrost-region pace. Soil warming was nearly keeping pace with air warming in the central and maritime parts of Alaska, but in the permafrost zone it lagged behind, even as it displayed its own version of polar amplification. In other words, the same latitudinal gradient that concentrates warming in the Arctic atmosphere is also concentrating warming in the Arctic ground.

Perhaps the most consequential discovery lies underground, literally. Conventional wisdom holds that soil temperatures become more stable with depth, because seasonal fluctuations damp out below the surface. The seasonal swings did indeed disappear in the deeper measurements, but the long-term warming trend did not. Instead, it became more obvious. In the permafrost region, every single weather station in the dataset showed a warming trend at a depth of four feet, while only 65 percent showed warming at two inches, where year-to-year variability masks the underlying signal. The deep soil, in effect, is integrating the warming signal over time and storing it.

Some of the winter figures are particularly sobering. In the permafrost region, Phillips notes, soils at four-foot depth warmed at rates of two to three degrees Celsius per decade during the winter months. Winter warming is especially significant for permafrost because it shortens the season during which the ground can refreeze, allowing thawed layers to persist and deepen year after year. Yet Phillips frames the deep soil’s heat storage in a dual light: the trend is upsetting, but it also represents one of the ways the planet is buffering the climate system, holding in the ground a substantial amount of heat that would otherwise remain in the atmosphere.

The scientific payoff of the study comes despite formidable logistical obstacles. Alaska is vast, remote, and expensive to instrument, and systematic weather stations that measure soil temperatures were only installed beginning in the late 1990s, leaving researchers with a comparatively short record relative to better-monitored regions. By synthesizing data from 43 stations and tracking soil temperatures at several depths across 27 years, the team assembled a dataset that few other studies of the air-soil temperature relationship in Alaska can match. The work relied on statistical analysis of these observational records rather than modeling, grounding its conclusions directly in what the ground and the air have actually done.

Why does deep soil warming matter so much? Permafrost, ground that remains frozen for two or more consecutive years, holds enormous quantities of organic carbon accumulated over millennia of slow decomposition in cold conditions. When permafrost thaws, microbes decompose that organic matter and release carbon dioxide and methane, both potent greenhouse gases, creating a feedback loop in which warming causes thaw, thaw causes emissions, and emissions cause more warming. The fact that warming is most pronounced precisely in the permafrost region, and that it penetrates to depths of four feet and beyond, means the zone of active carbon release is expanding downward and inland. At the same time, thawing ground loses the mechanical strength that frozen soil provides, undermining the stability of buildings, roads, and other infrastructure across the Arctic.

The researchers emphasize that the seasonal pattern of warming carries practical implications for adaptation. In the permafrost region, most of the warming occurred in winter, which will have pronounced effects on infrastructure that depends on frozen ground for stability. At lower latitudes, by contrast, most of the soil warming occurred in summer, which could extend Alaska’s growing season and reshape agricultural possibilities in a state where farming has historically been marginal. Knowing not just how much the soil is warming but when it is warming, Oliver argues, can help Alaskans plan for the changes ahead, from engineering decisions in permafrost towns to planting calendars in the south. As the Arctic continues to warm faster than the rest of the planet, the ground beneath Alaska is no longer a passive record of climate change but an active participant in it, and the measurements gathered from 43 remote stations now offer one of the clearest views yet of how quickly that transformation is unfolding.

Subject of Research: Long-term soil temperature warming across Alaskan permafrost and non-permafrost regions from 1997 to 2023

Article Title: In Alaska, soil in permafrost regions is warming the fastest

Article References: In Alaska, soil in permafrost regions is warming the fastest. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: permafrost, Alaska, soil warming, climate change, polar amplification, snow cover, greenhouse gases, carbon feedback, Arctic infrastructure, soil temperature, Frontiers in Climate, Washington State University

The physical scale of the loss is difficult to overstate. On average, glacier thickness across the monitoring network decreased by between 2.5 and 4 metres during the year, a figure that represents an enormous volume of water locked away for centuries in high-altitude ice. Some individual glacier tongues fared far worse, thinning by up to 10 metres of ice in a single season. At Konkordiaplatz, the vast accumulation basin at the heart of the Great Aletsch Glacier in the canton of Valais, photographs comparing the current ice extent with the average of measurements taken between 1953 and 1983 reveal a retreat that has accelerated dramatically in recent years, turning what was once a broad, snow-covered expanse into bare, debris-strewn ice.

For several of Switzerland’s smaller glaciers, 2026 marked the end of their existence entirely. The Bella Tola glacier in the canton of Valais and the Griessfirn in the canton of Glarus both melted away completely and permanently, joining a growing list of ice bodies that have vanished from the Swiss Alps within living memory. Their disappearance is not a temporary fluctuation but an irreversible threshold: once the ice mass falls below a critical size, the remaining ice can no longer survive the summer energy balance, and the glacier ceases to exist as a dynamic feature of the landscape. Researchers emphasize that these losses are permanent on any human timescale, since the climatic conditions that built these glaciers over millennia no longer prevail.

The most extreme melt of the year was concentrated in some of the country’s largest and most iconic ice masses. The Rhône Glacier, the Aletsch Glacier, and the Clariden Glacier, among others, all experienced their greatest melt on record in 2026. That these major glaciers, which possess far greater reserves of ice than the small cirque glaciers that vanished entirely, registered record losses underscores how exceptional the 2026 conditions were. Large glaciers typically respond more slowly to individual warm years because their sheer mass buffers short-term variability, so when even these giants post record ablation, the signal points to sustained, systemic forcing rather than ordinary weather noise.

The explanation for the devastation lies in the peculiar combination of the preceding winter and the following summer. The winter of 2025 to 2026 was among the ten least snowy winters in Switzerland since systematic snow measurements began. Snowpack is the glacier’s natural armour: a deep, bright snow layer reflects the majority of incoming solar radiation back to the atmosphere and shields the underlying ice from melting. When winter snowfall is meagre, glaciers enter the melt season already weakened, with dark older ice exposed earlier and absorbing more energy. The thin snow cover of 2025 to 2026 left Swiss glaciers unusually vulnerable before summer even began.

What followed was a summer of relentless heat and drought. Between May and September 2026, hot and dry conditions drove melt rates to excessive levels, and crucially, the melting extended all the way up to the highest elevations, where ice had historically remained safe even in warm years. For a total of 76 days, summer heatwaves pushed the freezing point above 4,000 metres, more than twice the long-term average and a Swiss record. Under such conditions, even the highest firn basins and summit icefields lost mass, leaving no refuge anywhere in the high Alps. By September, no snow remained even at 3,500 metres, stripping the glaciers of their protective white cover and exposing dark ice to direct solar radiation for the final weeks of the ablation season, which further amplified the melt through the feedback between surface darkening and energy absorption.

The data underpinning these findings come from GLAMOS, the Glacier Monitoring Network, which conducts long-term, systematic monitoring of Swiss glaciers and is headquartered at ETH Zurich together with the universities of Fribourg and Zurich. The network’s annual mass balance assessments rest on decades of field measurements, combining in situ stake readings and snow pit observations on representative glaciers with geodetic surveys that track changes in ice volume across the full inventory. This observational continuity is what allows scientists to place any single year, including 2026, in a reliable historical context and to distinguish genuine trends from year-to-year variability. The 2026 report, authored by Matthias Huss, Andreas Bauder, Andreas Linsbauer and Mylène Barandun, documents a mass balance year that ranks among the most extreme in the measurement record.

The meltwater released by the dying glaciers has tangible, if paradoxical, consequences for the country’s water supply. From July to September 2026, Swiss glaciers released approximately 2,200 billion litres of water, more than four times the annual drinking-water consumption of all Swiss households. During the hot, dry summer months, this outflow temporarily cushioned water shortages in downstream valleys, feeding rivers and reservoirs precisely when precipitation failed and demand peaked. In this sense, glacier melt acted as an emergency buffer against drought, a service the ice has provided to Alpine communities and the wider European river system for millennia.

Yet the researchers are clear that this buffering function is a wasting asset. The very melt that relieved summer water stress in 2026 is the mechanism by which the glacier reservoir is being emptied. As glacier area continues to shrink irreversibly, the volume of water the ice can release in dry summers declines with it. In the coming decades, the temporary relief of meltwater will give way to a new reality in which the late-summer contribution of ice to Alpine streams dwindles, with implications for hydropower, agriculture, ecology and water management far beyond the Swiss borders, since Alpine glaciers feed some of Europe’s major rivers.

The 2026 results thus capture a landscape in the midst of a transformation that is both rapid and permanent. Five years in which nearly a fifth of the national ice volume disappeared, individual glaciers blinking out of existence, and record melt on the largest ice bodies together illustrate how sensitive Alpine glaciers are to the combined effects of warm summers and snow-poor winters. The GLAMOS monitoring network’s measurements will continue to track the retreat, providing the observational foundation for understanding how much ice remains, how quickly it is going, and what the Alps will look like when the current era of accelerated loss runs its course. For Switzerland’s glaciers, 2026 was not an anomaly but another step in a sustained decline that the measurements show no sign of halting.

Subject of Research: Annual mass balance and record ice loss of Swiss glaciers in 2026

Article Title: Devastating year for Switzerland’s glaciers

Article References: Devastating year for Switzerland’s glaciers. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Swiss glaciers, GLAMOS, glacier melt, mass balance, climate change, Aletsch Glacier, Rhône Glacier, snow cover, heatwaves, water resources, ETH Zurich, Alps