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Modeling the short‐term (<50 years) evolution of glaciers is difficult because of issues related to model initialization and data assimilation. However, this timescale is critical, particularly for water resources, natural hazards, and ecology. Using a unique record of satellite remote‐sensing data, combined with a novel optimisation and surface‐forcing‐calculation method within the framework of the deep‐learning‐based Instructed Glacier Model, we are able to ameliorate initialization issues. We thus model the committed evolution of all glaciers in the European Alps up to 2050 using present‐day climate conditions, assuming no future climate change. We find that the resulting committed ice loss exceeds a third of the present‐day ice volume by 2050, with multi‐kilometer frontal retreats for even the largest glaciers. Our results show the importance of modeling ice dynamics to accurately retrieve the ice‐thickness distribution and to predict future mass changes. Thanks to high‐performance GPU processing, we also demonstrate our method's global potential. Plain Language Summary: Modeling glaciers is highly challenging over the next few decades because setting up models correctly is a big issue. This is unfortunate, because we really want to know what is going to happen on that timescale, as it will directly affect our lives, homes and jobs. We present a new modeling approach, taking advantage of new data and machine‐learning methods, that allows us to set our model up much more effectively. We thus work out how much ice will be lost in the European Alps between now and 2050, even if the climate does not change further, and find that a third of the ice will be lost, come what may. Even the largest glacier fronts will retreat by several kilometers. We show that modeling glaciers properly, with a well‐set‐up model, is really important to make accurate predictions. Key Points: We present a novel, fast, accurate deep‐learning method for glacier model initialization and forward simulation at a regional scaleWe calculate glacier climatic surface elevation change without needing calibration across the whole of the European Alps for the first timeWe find committed ice loss in the European Alps to be 34% by 2050, rising to 46% with linear extrapolation of 2000–2020 mass‐balance trends [ABSTRACT FROM AUTHOR]

Measuring surface mass-balance in the accumulation areas of glaciers is challenging because of the high spatial variability of snow accumulation and the difficulty of conducting annual field glaciological measurements. Here, we propose a method that can solve both these problems for many locations. Ground-penetrating radar measurements and firn cores extracted from a site in the French Alps were first used to reconstruct the topography of a buried end-of-summer snow horizon from a past year. Using these data and surface elevation observations from LiDAR and Global Navigation Satellite System instruments, we calculated the submergence velocities over the period between the buried horizon and more recent surface elevation observations. The differences between the changes in surface elevation and the submergence velocities were then used to calculate the annual surface mass-balances with an accuracy of ±0.34 m w.e. Assuming that the submergence velocities remain stable over several years, the surface mass-balance can be reconstructed for subsequent years from the differences in surface elevation alone. As opposed to the glaciological method that requires substantial fieldwork year after year to provide only point observations, this method, once submergence velocities have been calculated, requires only remote-sensing data to provide spatially distributed annual mass-balances in accumulation areas. [ABSTRACT FROM AUTHOR]

This study analyses the long‐term dynamics in the Chauvet glacial and periglacial system (southern French Alps) over seven decades (1948–2020), where several lake outburst floods have been documented since 1930. To accurately describe and explain the complex dynamics of this site, our multidisciplinary approach combines (1) photogrammetry of historical aerial photographs and modern high‐resolution satellite and UAV images, (2) geophysical surveys and (3) geomorphological mapping. We provide evidence for spatial and functional interactions between glacial and periglacial features, especially in the lower sector where different landforms with variable ice‐ and debris‐content and specific dynamics are interplayed. We found the highest thinning rates on ice‐rich terrain located in the central part of the valley bottom, which, together with bedrock morphology, most probably determine the location of the thermokarst. We also documented an overall acceleration of the creeping of the landforms after the 2000s, with a flow direction largely oriented towards the thermokarst depression. The outburst water flowed through a conduit whose successive opening and closure seem to mainly depend on the rate of lateral convergence of left‐ and right‐hand landforms and on the rate of ice melting (and roof collapse) along the conduit walls. Today, the site of Chauvet still represents a potential hazard for the region due to the large water storage capacity (up to 180 000 ± 450 m3) and the development of a predominantly bucket shape in the thermokarst sector. [ABSTRACT FROM AUTHOR]

The increased availability of high-resolution and high-quality digital elevation models (DEMs) allows for the investigation of small-scale glaciological changes and improved precision in geodetic mass-balance estimates. However, high precision and careful methodological choices are required to retrieve glacier-wide mass changes at annual to seasonal time scales. Here, we used a 7-year time series of 12 DEMs of the Glacier d'Argentière, in the French Alps, derived from the Pléiades optical satellites to assess the ability of sub-metre stereoscopic satellite images to retrieve annual-to-seasonal mass balances. We found good agreement between the five annual and the four winter mass-balance values estimated using a geodetic method and those of in situ glaciological measurements: mean values via the geodetic method are −0.66 m w.e. and 1.47 m w.e. for annual and winter balances, respectively; mean absolute discrepancies are 0.25 m w.e. (annual) and 0.36 m w.e. (winter). Our study identified three main limitations of this methodology: (i) the intrinsic DEM precision; (ii) the lack of control over the satellite acquisition dates; and (iii) the density assumption. The consistency between the methods demonstrates the potential of short time-scale glacier mass-balance monitoring using very high-resolution satellite images. [ABSTRACT FROM AUTHOR]

Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. While plant-survey-based approaches applied along chronosequences provide invaluable information on plant communities, the "space-for-time" approach assumes environmental uniformity and equal ecological potential across sites and does not account for spatial variability in initial site conditions. Remote sensing provides a promising avenue for assessing plant colonization dynamics using a so-called "real-time" approach. Here, we combined 36 years of Landsat imagery with extensive field sampling along chronosequences of deglaciation for eight glacier forefields in the southwestern European Alps to investigate the heterogeneity of early plant succession dynamics. Based on the two complementary and independent approaches, we found strong variability in the time lag between deglaciation and colonization by plants and in subsequent growth rates and in the composition of early plant succession. All three parameters were highly dependent on the local environmental context, i.e., neighboring vegetation cover and energy availability linked to temperature and snowmelt gradients. Potential geomorphological disturbance did not emerge as a strong predictor of succession parameters, which is perhaps due to insufficient spatial resolution of predictor variables. Notably, the identity of pioneer plant species was highly variable, and initial plant community composition had a much stronger influence on plant assemblages than elapsed time since deglaciation. Overall, both approaches converged towards the conclusion that early plant succession is not stochastic as previous authors have suggested but rather determined by local ecological context. We discuss the importance of scale in deciphering the complexity of plant succession in glacier forefields and provide recommendations for improving botanical field surveys and using Landsat time series in glacier forefield systems. Our work demonstrates complementarity between remote sensing and field-based approaches for both understanding and predicting future patterns of plant succession in glacier forefields. [ABSTRACT FROM AUTHOR]

Documenting glacier surface flow velocity from a longer-term perspective is highly relevant to evaluate the past and current state of glaciers worldwide. For this purpose, satellite data are widely used to obtain region-wide coverage of glacier velocity data. Well-established image correlation methods allow for the automated measurement of glacier surface displacements from satellite data (optical and radar) acquired at different dates. Although computationally expensive, image correlation is nowadays relatively simple to implement and allows two-dimensional displacement measurements. Here, we present a data set of annual glacier surface flow velocity maps at the European Alps scale, covering the period 2015–2021 at a 50 m × 50 m resolution. This data set has been quantified by applying the normalized cross-correlation approach on Sentinel-2 optical data. Parameters of the cross-correlation method (e.g., window size, sampling resolution) have been optimized, and the results have been validated by comparing them with in situ data on monitored glaciers showing an RMSE of 10 m/yr. These data can be used to evaluate glacier dynamics and its spatial and temporal evolution (e.g., quantify mass fluxes or calving) or can be used as an input for model calibration/validation or for the early detection of regional hazards associated with glacier destabilization. Dataset:  https://doi.org/10.57745/XHQ7TL Dataset License: CC BY NC 4.0 [ABSTRACT FROM AUTHOR]

Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. While plant survey-based approaches applied along chronosequences provide invaluable information on plant communities, the "space-for-time" approach assumes environmental uniformity and equal ecological potential across sites and does not account for spatial variability in initial site conditions. Remote sensing provides a promising avenue for assessing plant colonisation dynamics using a so-called "real-time" approach. Here, we combined 36 years of Landsat imagery with extensive field sampling along chronosequences of deglaciation for eight glacier forefields in the south- western European Alps to investigate the heterogeneity of early plant succession dynamics. Based on the two complementary and independent approaches, we found strong variability in the time lag between deglaciation and colonisation by plants and in subsequent growth rates, and in the composition of early plant succession. All three parameters were highly dependent on the local environmental context, i.e., local vegetation surrounding the forefields and energy availability linked to temperature and snowmelt gradients. Potential geomorphological disturbance did not emerge as a strong predictor of succession parameters, perhaps due to insufficient spatial resolution of predictor variables. Notably, elapsed time since deglaciation showed no consistent relationship to plant assemblages, i.e., we did not identify a consistent order of successional species across forefields as a function of time. Overall, both approaches converged towards the conclusion that early plant succession is not stochastic as previous authors have suggested but rather deterministic. We discuss the importance of scale in deciphering the unique complexity of plant succession in glacier forefields and provide recommendations for improving botanical field surveys and using Landsat time series in glacier forefields systems. Our work demonstrates complementarity between remote sensing and field-based approaches for both understanding and predicting future patterns of plant succession in glacier forefields. [ABSTRACT FROM AUTHOR]

Ongoing hydrological alterations due to climate change and anthropogenic uses of water have major implications for freshwater biodiversity. Quantifying the relative effects of environmental variables on macroinvertebrates is required to predict biological responses to hydrological alterations. To date, no study simultaneously examined the effects of physico‐chemistry, hydraulics, and hydrology on the distribution of alpine macroinvertebrate communities and taxa. In this study, we aimed to quantify the relative correlation between these environmental variables and macroinvertebrate community composition and structure.We sampled macroinvertebrates at 66 stream sites located in three catchments in the French Alps. We characterised the proximate habitat at each site using 11 variables describing measured physico‐chemistry and hydraulics, and simulation‐based hydrology. We described relationships between community structure and the environment using a co‐inertia analysis and modelled individual taxa abundance with generalised linear mixed models.The co‐inertia revealed a significant co‐structure between the environmental and macroinvertebrate matrices. Glacier‐influenced sites with high turbidity and summer flow exhibited similar community composition with low total abundance. Sources at high altitude and sites with low glacial influence, exhibiting high summer flow and flow velocity, were dominated by Diamesinae, Rhithrogena spp., Dictyogenus spp., and Baetis alpinus. Streams fed by rainfall/snowmelt and valley sources, associated with higher temperature, conductivity, and monthly discharge variability were characterised by higher richness and abundances. Models indicated that the three types of proximate habitat variables significantly contributed to the macroinvertebrate distribution. Turbidity was strongly negatively associated with macroinvertebrate abundances. Increasing flow velocity and summer flow had significant (mainly negative) effect in 43% of models.The co‐structure between communities and proximate habitat variables was shared by the three catchments. For most individual taxa, catchment identity did not influence abundance models and cross‐validations indicated transferable effects of proximate habitat variables among alpine catchments.Our results can be used to infer responses of alpine macroinvertebrates to multivariate environmental changes. Understanding the relationships between macroinvertebrates and environmental variables help to predict how communities and taxa will be affected by habitat alterations due to ongoing hydrological changes and resulting physico‐chemical conditions. [ABSTRACT FROM AUTHOR]

Documenting the inter-annual variability and the long-term trend of the glacier snow line altitude is highly relevant to document the evolution of glacier mass changes. Automatically identifying the snow line on glaciers is challenging; recent developments in machine learning approaches show promise to tackle this issue. This manuscript presents a proof of concept of machine learning approaches applied to multi-spectral images to detect the snow line and quantify its average altitude. The tested approaches include the combination of different image processing and classification methods, and takes into account cast shadows. The efficiency of these approaches is evaluated on mountain glaciers in the European Alps by comparing the results with manually annotated data. Solutions provided by the different approaches are robust when compared to the ground truth's snow lines, with a Pearson's correlation ranging from 79% to 96% depending on the method. However, the tested approaches may fail when snow lines are not continuous or exhibit a strong change of elevation. The major advantage over the state of the art is that the proposed approach does not require one calibration per glacier. [ABSTRACT FROM AUTHOR]

Glaciers and ice caps are experiencing strong mass losses worldwide, challenging water availability, hydropower generation, and ecosystems. Here, we perform the first-ever glacier evolution projections based on deep learning by modelling the 21st century glacier evolution in the French Alps. By the end of the century, we predict a glacier volume loss between 75 and 88%. Deep learning captures a nonlinear response of glaciers to air temperature and precipitation, improving the representation of extreme mass balance rates compared to linear statistical and temperature-index models. Our results confirm an over-sensitivity of temperature-index models, often used by large-scale studies, to future warming. We argue that such models can be suitable for steep mountain glaciers. However, glacier projections under low-emission scenarios and the behaviour of flatter glaciers and ice caps are likely to be biased by mass balance models with linear sensitivities, introducing long-term biases in sea-level rise and water resources projections. Deep learning unveils a nonlinear sensitivity of glacier mass changes to future climate warming, with important implications for water resources and sea-level rise coming from glaciers and particularly ice caps. [ABSTRACT FROM AUTHOR]

Spatio-temporal variability of the winter surface mass balance is a major uncertainty in the modelling of annual surface mass balance. Moreover, its measurement at high spatio-temporal resolution (sub-200 m) is very useful to force, calibrate or validate models. This study presents the results of year-round field campaigns to study the evolution of the surface mass balance in a ~2 km2 portion of the accumulation zone of the Mer de Glace (France). It is based on repeated LiDAR acquisitions, submergence-velocity measurements and meteorological records. The two methods used to quantify submergence velocities show good agreement. They present a linear temporal evolution without significant seasonal changes but display significant spatial variability. We conclude that a dense network of submergence velocity measurements is required to reduce the uncertainties when computing winter and annual surface mass balance from digital elevation model differencing. Finally, a height spatio-temporal variability of the winter surface mass balance is highlighted (e.g., a std dev. of 0.92 m in April) even though the topography is homogeneous (std dev. of 25 m). Attempts to relate this variability to different morpho-topographic variables and wind-related indexes show the need for studies conducted at the snowfall event scale to obtain a better understanding of the variability in mass balance at the glacier scale. [ABSTRACT FROM AUTHOR]

Glacier mass balance (MB) data are crucial to understanding and quantifying the regional effects of climate on glaciers and the high-mountain water cycle, yet observations cover only a small fraction of glaciers in the world. We present a dataset of annual glacier-wide mass balance of all the glaciers in the French Alps for the 1967–2015 period. This dataset has been reconstructed using deep learning (i.e. a deep artificial neural network) based on direct MB observations and remote-sensing annual estimates, meteorological reanalyses and topographical data from glacier inventories. The method's validity was assessed previously through an extensive cross-validation against a dataset of 32 glaciers, with an estimated average error (RMSE) of 0.55 mw.e.a-1 , an explained variance (r2) of 75 % and an average bias of -0.021 mw.e.a-1. We estimate an average regional area-weighted glacier-wide MB of -0.69 ± 0.21 (1 σ) mw.e.a-1 for the 1967–2015 period with negative mass balances in the 1970s (-0.44 mw.e.a-1), moderately negative in the 1980s (-0.16 mw.e.a-1) and an increasing negative trend from the 1990s onwards, up to -1.26 mw.e.a-1 in the 2010s. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for the 1967–2015 period are the Chablais (-0.93 mw.e.a-1), Champsaur (-0.86 mw.e.a-1), and Haute-Maurienne and Ubaye ranges (-0.84 mw.e.a-1 each), and the ones presenting the lowest mass losses are the Mont-Blanc (-0.68 mw.e.a-1), Oisans and Haute-Tarentaise ranges (-0.75 mw.e.a-1 each). This dataset – available at 10.5281/zenodo.3925378 – provides relevant and timely data for studies in the fields of glaciology, hydrology and ecology in the French Alps in need of regional or glacier-specific annual net glacier mass changes in glacierized catchments. [ABSTRACT FROM AUTHOR]

The ongoing glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring, modelling purposes (e.g. determination of run-off, mass balance, or future glacier extent), and other applications. With the launch of the first Sentinel-2 (S2) satellite in 2015, it became possible to create a consistent, Alpine-wide glacier inventory with an unprecedented spatial resolution of 10 m. The first S2 images from August 2015 already provided excellent mapping conditions for most glacierized regions in the Alps and were used as a base for the compilation of a new Alpine-wide glacier inventory in a collaborative team effort. In all countries, glacier outlines from the latest national inventories have been used as a guide to compile an update consistent with the respective previous interpretation. The automated mapping of clean glacier ice was straightforward using the band ratio method, but the numerous debris-covered glaciers required intense manual editing. Cloud cover over many glaciers in Italy required also including S2 scenes from 2016. The outline uncertainty was determined with digitizing of 14 glaciers several times by all participants. Topographic information for all glaciers was obtained from the ALOS AW3D30 digital elevation model (DEM). Overall, we derived a total glacier area of 1806±60 km 2 when considering 4395 glaciers >0.01 km 2. This is 14 % (-1.2 % a -1) less than the 2100 km 2 derived from Landsat in 2003 and indicates an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. It is a lower-bound estimate, as due to the higher spatial resolution of S2 many small glaciers were additionally mapped or increased in size compared to 2003. Median elevations peak around 3000 m a.s.l., with a high variability that depends on location and aspect. The uncertainty assessment revealed locally strong differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitized by different analysts. The inventory is available at 10.1594/PANGAEA.909133 (Paul et al., 2019). [ABSTRACT FROM AUTHOR]

The on-going glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring or modeling purposes (e.g. determination of run-off, mass balance, or future glacier extent) and other applications. With the launch of the first Sentinel-2 (S2) satellite in 2015, it became possible to create a consistent, Alpine-wide glacier inventory with an unprecedented spatial resolution of 10 m. Fortunately, already the first S2 images acquired in August 2015 provided excellent mapping conditions for most of the glacierised regions in the Alps. We have used this opportunity to compile a new Alpine-wide glacier inventory in a collaborative team effort. In all countries, glacier outlines from the latest national inventories have been used as a guide to compile a consistent update. However, cloud cover over many glaciers in Italy required including also S2 scenes from 2016. Whereas the automated mapping of clean glacier ice was straightforward using the band ratio method, the numerous debris-covered glaciers required in-tense manual editing. The uncertainty in the outlines was determined with a multiple digitising experiment of 14 glaciers by all participants. Topographic information for all glaciers was derived from the ALOS AW3D30 DEM. Overall, we derived a total glacier area of 1806 ± 60 km² when considering 4394 glaciers > 0.01 km². This is 14 % (−1.2 %/a) less than the 2100 km² derived from Landsat scenes acquired in 2003 and indicating an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. Due to the higher spatial resolution of S2 many small glaciers were additionally mapped in the new inventory or increased in size compared to 2003. An artificial reduction to the former extents would thus result in an even higher overall area loss. Still, the uncertainty assessment revealed locally considerable differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitised by different analysts. [ABSTRACT FROM AUTHOR]

Less than 0.25% of the 250 000 glaciers inventoried in the Randolph Glacier Inventory (RGI V.5) are currently monitored with in situ measurements of surface mass balance. Increasing this archive is very challenging, especially using time-consuming methods based on in situ measurements, and complementary methods are required to quantify the surface mass balance of unmonitored glaciers. The current study relies on the so-called albedo method, based on the analysis of albedo maps retrieved from optical satellite imagery acquired since 2000 by the MODIS sensor, on board the TERRA satellite. Recent studies revealed substantial relationships between summer minimum glacier-wide surface albedo and annual surface mass balance, because this minimum surface albedo is directly related to the accumulation-area ratio and the equilibrium-line altitude. On the basis of 30 glaciers located in the French Alps where annual surface mass balance data are available, our study conducted on the period 2000-2015 confirms the robustness and reliability of the relationship between the summer minimum surface albedo and the annual surface mass balance. For the ablation season, the integrated summer surface albedo is significantly correlated with the summer surface mass balance of the six glaciers seasonally monitored. These results are promising to monitor both annual and summer glacier-wide surface mass balances of individual glaciers at a regional scale using optical satellite images. A sensitivity study on the computed cloud masks revealed a high confidence in the retrieved albedo maps, restricting the number of omission errors. Albedo retrieval artifacts have been detected for topographically incised glaciers, highlighting limitations in the shadow correction algorithm, although interannual comparisons are not affected by systematic errors. [ABSTRACT FROM AUTHOR]

Based on an extensive dataset of surface mass balances (SMB) from four glaciers in the French Alps for the period 1995–2012 and in the framework of enhanced temperature-index models, we investigate the sensitivity of seasonal glacier SMB to temperature, solar radiation, precipitation and topographical variables. Our results reveal strong correlations between winter SMB and precipitation, although the precipitation gradient cannot explain the high-accumulation rates. Based on the available point measurements, we found no relevant relationship between winter SMB and topographical variables. Temperature was found to be the main driver of ice/snow ablation while solar radiation was found to strongly influence the spatial distribution of summer SMB. We compared the ability of several enhanced temperature-index models to accurately simulate point SMB and glacier-wide MB. Our analyses revealed that the uncertainties in the simulated annual SMB due to winter SMB uncertainties are larger than differences between models and prevented us from concluding, which model is the most suitable. In contrast with results of previous studies, including solar radiation in melt models did not improve the performances when modelling glacier-wide MB. We conclude that a classical degree-day model is sufficient to simulate the long-term glacier-wide MB if the underlying processes are not required to be resolved. [ABSTRACT FROM AUTHOR]

The most recent and complete French glacier inventory was previously the Vivian database, dating from the end of the 1960s but incorporated in the World Glacier Inventory database at the end of the 1990s. Because of the important changes in glacier extent over recent decades an update of the inventory of glaciers of the French Alps was made in a digital vector format (with the associated database) for several dates covering the last 40 years. Such a multitemporal glacier inventory matches a key demand of the Global Terrestrial Network for Glaciers and the Global Land Ice Measurements from Space initiative (GLIMS). Topographical maps, aerial photographs and satellite images were used to map the extent of glaciers using both manual and automatic methods; and the database was generated considering the design of the GLIMS database. Glaciers in the French Alps covered 369 km² in 1967/71, 340 km² in 1985/86, 300 km² in 2003, and 275 km² in 2006/09. This represents a decrease in surface area of about 25% over the entire study period. Acceleration in glacier shrinkage during the study period was revealed, probably linked to the increase in average air temperature in the 20th century, which has been particularly pronounced since the 1970s. The behaviour of glaciers of the French Alps is in agreement with that of glaciers observed by other studies across the European Alps. We also report the distribution of the morpho-topographic variables (aspect, elevation, etc.) of glaciers of the French Alps for the period 2006/09, and analyse changes of these variables in the last four decades. [ABSTRACT FROM AUTHOR]

• We report on 188 soil samples from 9 chronosequences of deglacierization in the Andes and Alps. • Parent material strongly controls soil properties in the early stage of pedogenesis. • Potential As, Cu, Mo, and Mn soil toxicity is reported in Andean proglacial soils. • Initial differences in soil properties dissipate along 120-year chronosequences. New soils formed after glacier retreat can provide insights into the rates of soil formation in the context of accelerated warming due to climate change. Recently deglacierized terrains (since the Little Ice Age) are subject to weathering and pedogenesis, and freshly exposed sediments are prone to react readily with the environment. This study aims to determine the impact of parent material and time on soil physical and chemical properties of nine proglacial landscapes distributed in the Tropical Andes and Alps. A total of 188 soil samples were collected along chronosequences of deglacierization and from sites that differed in terms of parent material and classified following three parent material groups: (1) Granodiorite-Tonalite (GT), (2) Gneiss-Shales-Schists (GSS), and (3) Mont-Blanc Granite (MBG). We determined physical and chemical soil properties such as contents of clay, silt, sand, organic carbon, bulk density (BD), pH, extractable cation (exCa, exMg, exK), elemental composition by X-ray fluorescence (Al, Si, P, S, K, Ca, Mn, Fe, Cu, Zn, As, Mo, Hg, Pb) and ICP-MS (Al, Ca, Cu, Fe, K, Mg, Mn, Mo, Na, P, S, Zn), and mineral phase (XRD diffraction analysis). Parent material-controlled particle-size distribution, SOC, pH, available P, exCa, and exMg, whereas time since deglacierization only affected SOC and P, and exMg globally. Most of the significant differences in soil properties between parent material groups occurred within the first 17 years after deglacierization, and then we observed a homogenization between sites. While the higher SOC and P contents observed within the GT Andean sites might be due to the parent material composition leading to faster initial soil formation, we identified potential As, Cu, Mo, and Mn toxicity within those soils. Our study highlights the need to investigate further proglacial soil's buffering capacity and carbon sequestration to globally inform the conservation and management of novel proglacial ecosystems. [ABSTRACT FROM AUTHOR]

• 3 years of soil temperatures recorded in nine proglacial landscapes. • 188 soil samples collected between the Tropical Andes and European Alps and analyzed. • Environmental context drives differences in proglacial pedogenesis between regions. • Soil temperature has a strong control on the transformation rate of soil properties. • Overly high maximum soil temperatures reduce SOC accretion and N mineralization. Climate warming has accelerated the retreat of mountain glaciers worldwide, exposing new areas to weathering, vegetation colonization, and soil formation. In light of probable climate changes such as warming and new extremes, understanding the factors that control soil organic carbon (SOC) and nitrogen build-up is crucial to comprehend proglacial soils and ecosystem formation. To this end, we examine the evolution of SOC, nitrogen (total N and NH 4 +), and phosphorus (available P) along nine 120-year chronosequences of deglacierization distributed between the European Alps and Tropical Andes. Our dataset includes geochemical analyses of 188 soil samples, in situ soil temperature data for the period 2019–2022, and hydrographic variables. Although time controls proglacial soil development at all sites, our study highlights distinct pedogenesis dynamics between proglacial landscapes depending on the micro and macro environmental context. Differences in soil development were strongly driven by growing degree days (GDD), maximum soil temperature, and parent material. Notably, we identified a positive effect of GDD on SOC and N (total N and NH 4 +), while our results indicate a negative effect of maximum soil temperature on SOC and NH 4 +, suggesting that overly high temperatures reduce microbial mineralization and organic matter input to the soil matrix. We reported the presence of higher initial SOC, total N, and NH 4 + in the Andean sites than in the Alps sites, suggesting enhanced soil development at the Andean locations. This comparative study suggests the relative importance of maximum temperature and initial site conditions (e.g., parent materials, glacier biomes) during proglacial pedogenesis. Our findings highlight that soil temperature modulates pedogenesis in a complex way and suggest avoiding simply associating greater soil development with higher soil temperature in proglacial landscapes. [ABSTRACT FROM AUTHOR]