Institute of Science and Technology Austria

The Pamir Mountains are a key focus of research on the response of glaciers to climate change in High Mountain Asia. They constitute an important mountain water tower that is highly vulnerable to future climatic and environmental change, and they host glaciers that experienced limited but increasing mass loss during 2000-2019. Few studies have constrained mass balance in detail within the region, and these have highlighted strong spatio-temporal variability. Quantification of glacier mass changes and their drivers is challenged by sparse in situ data, strong East-West gradients of temperature and precipitation, and complex glacier processes including debris cover, surging behavior, and collapse features. In this study, we focus on 41 glaciers in the Sangvor region of the western Pamir for 2003-2025 to: i) evaluate the robustness of previous estimates of mass losses for 2000-2019, ii) identify glacier trajectories beyond 2019, and iii) understand the influence of surging glaciers on the observed mass changes. We adopted a stereo image-processing workflow from the Ames Stereo Pipeline and generated DEMs from SPOT5, SPOT6, and Pléiades stereo satellite imagery. Glacier mass balance for each time series was derived using current best practices for DEM co-registration, bias correction, and uncertainty propagation. Additionally, we applied new algorithms for jitter correction, seasonal snow correction based on in-situ data, and a duration-dependent correction scheme for volume-to-mass conversion uncertainties. Our results provide a time series of high-resolution geodetic surface height changes and glacier mass balance over seven sub-periods spanning 2003 to 2025: -0.19 ± 0.04 m w.e. a-1 for 2003-2019, -0.63 ± 0.05 m w.e. a-1 for 2019-2025, and -0.28 ± 0.02 m w.e. a-1 for the entire period of 2003-2025 for three key intervals. Our 2003-2019 results agree with the mean mass balance measured by ASTER for 2000-2019 (-0.21 m w.e. a-1) and with the temporal trend. We highlight a sharp mass gain (0.16 ± 0.03 m w.e. a-1) between 2014 and 2019, followed by a pronounced progressive shift toward negative mass balances. During 2019-2025, characterized by exceptionally warm and dry conditions, the mass balance has been increasingly negative despite higher uncertainty in our annual estimates, reaching its maximum loss of -1.45 (+0.51/-0.19) m w.e. a-1 in 2024-2025. We also find no statistically significant difference between the mass balance of surging glaciers and the regional mean for the periods of our study. Overall, our results reveal strong temporal variability in glacier mass balance in the region. While long-term mean mass balances agree with previous ASTER-based estimates, our high-resolution geodetic time series resolves their short-term variability, illuminating a complex evolution that includes a marked mass gain for 2014-2019 and a rapid shift toward strongly negative balances thereafter. Comparison with reanalysis data suggests that this variability is more closely linked to precipitation anomalies than to temperature, suggesting a dominant role of mass accumulation and snowfall variability. These results demonstrate the value of high-resolution stereo imagery and motivate extension to the wider Pamir region and can form a new, high-resolution baseline for modelling projections of future glacier changes in the region.

Meltwater from High Mountain Asia (HMA) provides water resources to large downstream populations of central, southern and eastern Asia. Recent decades have seen critical changes in the health of the high mountain cryosphere of the region, which poses risks to those dependent upon its water supply. Glaciers of HMA have been observed to change mass at varying rates regionally, likely caused by a combination of heterogeneous climate change and region-specific glacier sensitivities. Regional studies on glacier sensitivities have been limited by computational power to rely on degree day models with higher dependence on calibration and equifinality issues. The same computational limits and data availability have limited energy balance studies to short periods, localised domains or coarsely resolved glacier regions. Explicit representation of glacier specific hypsometry and surface characteristics allows the inclusion of non-linear sensitivity of mass balance with elevation, as well as the inclusion of effects of debris cover. Here we provide a new regional picture of HMA glacier sensitivity to climate using a physically-based high resolution energy and mass balance model (Tethys & Chloris).We run the model for 50 glaciers in each glacier subregion of HMA, chosen to be representative in terms of glacier area, median elevation, debris area and mean debris thickness. Each glacier is modelled using a clustering of points to discretise its surface, where the number of points is optimised depending on glacier size. We first build a baseline of glacier response to present climate (2000-2010) by forcing the model with bias-corrected hourly ERA5-Land data. For the bias correction, a precipitation factor and temperature bias is optimised for each glacier so that model results match against remotely sensed observations of glacier mass balance, snow line altitude and surface albedo.To determine glacier sensitivities and their drivers, we perturb temperature and precipitation annually to quantify the mass balance changes at the glacier and regional scale. Further experiments perturbing seasonal climatic signals reveal the relevance and relative impacts of temperature and precipitation changes for regions previously identified as experiencing anomalous mass balance behaviour, such as the Pamir-Karakoram. We analyse the contribution of the main energy fluxes and melt components to the glacier-wide mass balance and quantify the relative influence of precipitation solid fraction and sublimation under these different climate perturbation scenarios. We study the sensitivity of the accumulation area ratio and the glacier equilibrium line altitude as indicators of the regional patterns in glacier health, its year-to-year variability and susceptibility to abrupt shifts under climatic extremes. With this, we identify and explain which regions may be most susceptible to future climate change signals, with an unprecedented attribution to the underlying glacier characteristics and changes in physical processes.

Mountain regions in Europe are experiencing changes in temperature and precipitation patterns, as well as an increasing frequency and intensity of droughts. Periods of low precipitation reduce winter snow accumulation, while anomalous hot periods during spring and summer accelerate snowmelt, altering the water cycle and discharge patterns of mountain systems and their downstream areas. The combination of warmer and drier conditions increases water scarcity, reduces crop yields and decreases hydropower generation, with reductions in streamflow and groundwater recharge. Implementing high-resolution land surface models allow us to predict future conditions by capturing the complex behavior of flow paths and water storage across entire regions. Consequently, these models facilitate the study of future droughts and their impact on hydrological conditions within the catchment. We identify a number of droughts including snow droughts in the Central Apennines of Italy, and investigate their compounded effects on the hydrosphere, biosphere and pedosphere using a fully distributed, mechanistic land surface model (Tethys-Chloris) over two decades (from 2000 to 2020). Our model configuration resolves energy budgets and mass balances at an hourly timestep and 250 m resolution to simulate processes such as snowmelt, sublimation and plant transpiration. We force the model using a combination of stations and bias-corrected ERA5Land reanalysis data and evaluate it against streamflow measurements, snow depth, soil moisture and remote sensing products of snow-covered area and leaf area index. We analyze distributed simulations of snow depth, snow water equivalent, soil moisture, lateral subsurface water fluxes and surface temperature to obtain a highly resolved picture of the functioning of the mountain hydrological system. Our analysis shows how droughts produced by a reduction of snow accumulation, precipitation or warm temperatures produce runoff deficits and positive anomalies of evapotranspiration at high elevations. We focus in particular on the effect of evaporative fluxes in reducing water yields. Our findings indicate that warm periods lead to enhanced evapotranspiration at elevations between 1500 and 2500 m a.s.l. We also investigate the contrasting effect of snow on this so-called drought paradox, as snow provides water vital to plant functioning, but limits the growing season length. This phenomenon ultimately reduces downstream water availability in mountain regions, which impacts water security for mountain-dependent communities and ecosystems. As such, our study provides an entirely new understanding of the eco-hydrological functioning of the Central Apennines water system under drought stresses, and establishes a baseline of unprecedented resolution in time and space to predict the ecological and hydrological impacts of future droughts.

Debris-covered glaciers play an important role in alpine hydrology, yet the origin and transport pathways of debris within the glacier remain difficult to constrain. This is a major limitation in glacier evolution models, many of which have tended to ignore debris transient effects, especially for assessment of catchment hydrological processes. Supraglacial debris coverage is an integrated signal of debris supply, climate forcing, glacier geometry and debris physical properties, all of which vary in time, resulting in a complex inverse problem for model-based reconstructions.Here, we apply the Instructed Glacier Model (IGM) with a newly implemented Lagrangian debris transport module combined with an ensemble of climate forcing, to explore constraints on debris input at Oberaletsch Glacier in the Swiss Alps. This framework allows debris particles to be tracked within a dynamically evolving glacier geometry and enables a likelihood-based assessment of inferred debris source regions across the ensemble. Rather than seeking a unique reconstruction, we identify spatially persistent and statistically robust seeding areas that are compatible with the observed historical evolution of debris extent derived from debris-cover maps for 1965, 1985, 1995, 2000, 2010 and 2015. These areas are identified by tracing debris particles backward from their final positions within the observed debris-covered zone to their upstream seeding locations.Our results show that our inverse filtering strategy effectively identifies potential debris input zones that are primarily controlled by glacier dynamics and geometry. Notably, including or excluding the effect of debris on surface mass balance does not significantly alter the reconstructed debris extent in the ablation zone, highlighting the dominant role of ice flow in shaping supraglacial debris patterns at glacier scale. The reconstructed debris input patterns allow us to reproduce the observed historical evolution of debris extent and glacier geometry with good agreement. Debris extent matching between simulations and observations reaches around 80%, while the percentage of the total particles ending up in the target debris area remains above 70%. Ongoing work addresses the reconstruction of debris thickness, which is sensitive to both the debris weight (i.e. the assigned debris volume) prescribed for each particle and, crucially, the climatic forcing, requiring an iterative approach to capture the full transient characteristics of the glacier debris cover.This study demonstrates that ensemble-based Lagrangian modelling provides a powerful framework to constrain debris input to glaciers. By explicitly coupling debris transport to the evolving glacier dynamics, this approach opens new perspectives for interpreting present-day debris cover and for projecting the future evolution of debris-covered glaciers under changing climatic conditions.

Deep convective systems (DCSs) play a crucial role in the tropical hydrological cycle and radiative budget [1,2]. In particular, the largest and longest-lived of those cloud systems contribute to a high fraction of the extreme precipitation in the Tropics [3] Therefore understanding what drives these types of systems is crucial.To that end, Abramian et al. 2025 [4] developed a new method to predict the maximum area of DCSs using the DYAMOND-Summer simulation with the cloud-resolving global model SAM, and the TOOCAN algorithm to track cloud systems [5]. The method uses simple machine learning models, trained on information on the early stage of the systems and their surrounding environment, including dynamical and thermodynamical variables, morphological features of the systems and the characteristics of their neighbors.We investigated whether this method would work in observations too, using DCS tracks identified by TOOCAN in satellite data [6], combined with ERA5 data for the physical variables. For both observations and DYAMOND we used both a Lasso linear regression and two different deep neural networks.Furthermore we aimed at understanding which physical variables constrain the most the maximum area of the systems, and to that end we used an explainable AI method, the integrated gradients ([7]) to assess which physical variables contributed the most to the model prediction.Firstly, we managed to achieve good predictivity scores for both the non-linear models and both the datasets and we obtained quite robust results in terms of feature importance, with the pre-storm environmental CAPE and deep shear playing a pivotal positive role to achieve a large maximum area, while the presence of neighboring systems was one of the main negative contributors.Finally, we tested the ML results by looking at composites of the most important variables in the observational dataset: for example pre-storm CAPE composite showed significantly higher than average values for the largest systems. [1] Nesbitt, S. W., R. Cifelli, and S. A. Rutledge, 2006: Storm Morphology and Rainfall Characteristics of TRMM Precipitation Features. [2] Bony, S., Semie, A., Kramer, R. J., Soden, B., Tompkins, A. M., & Emanuel, K. A. (2020). Observed modulation of the tropical radiation budget by deep convective organization and lower-tropospheric stability. [3] Remy Roca and Thomas Fiolleau. Extreme precipitation in the tropics is closely associated withlong-lived convective systems.[4] S. Abramian, C. Muller, C. Risi, et al. How key features of early development shape deepconvective systems.[5] Thomas Fiolleau and Remy Roca. An algorithm for the detection and tracking of tropicalmesoscale convective systems using infrared images from geostationary satellite. [6] T. Fiolleau and R. Roca. A database of deep convective systems derived from the intercalibratedmeteorological geostationary satellite fleet and the toocan algorithm (2012–2020). [7] Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks.

How is lightning triggered on the microscale? Despite decades of research, this question remains unanswered [1]. In our experiment, we use optical tweezers to gain a better understanding of the microscale physics of electric charging and discharging by levitating individual SiO2 particles in the micrometer size range and observing their charging and discharging dynamics over days-to-weeks time periods and with elementary-charge resolution. Our approach allows us to study these processes without losing information to ensemble averages or external interference from other particles or substrates [2]. Using two-photon absorption from the trapping laser [3] we can charge the trapped particle at different rates and to different values, observing every charging and discharging event along the way. This new approach lets us watch, in real time, how a micron-scale airborne particle gains and loses charge, observing its electric evolution all the way from the neutral state to the point where it undergoes electric discharge. By studying the charging behavior of the particle and the spontaneous discharges it experiences, we hope to contribute to a better understanding of the microphysical processes involved in lightning initiation and adjacent electrical phenomena in the atmosphere.Figure 1: Time vs. electric charge curve of a single SiO2 sphere (d = 0.69 µm) showing spontaneous discharges. This project has received funding from the European Research Council (ERC) under the European Union’s Starting Grant (A. Stoellner, I.C.D. Lenton & S.R. Waitukaitis received funding from ERC No. 949120, C. Muller received funding from ERC No. 805041). [1] Dwyer, J. R., & Uman, M. A. (2014), Physics Reports, 534(4), 147–241.[2] Ricci, F. et al. (2019), Nano Letters 19, 6711.[3] Stoellner, A. et al. (2025), Phys. Rev. Lett. 135(21), 218202.

Farkas established that a system of linear inequalities has a solution if and only if we cannot obtain a contradiction by taking a linear combination of the inequalities. We state and formally prove several Farkas-like theorems over linearly ordered fields in Lean 4. Furthermore, we extend duality theory to the case when some coefficients are allowed to take "infinite values". Code: https://github.com/madvorak/duality/tree/v3.2.0

Pioneer transcription factors (TFs) possess the ability to read out DNA motifs embedded within nucleosomes, driving changes in gene expression during cellular differentiation and reprogramming. Here, we present selected engagement on nucleosome sequencing (SeEN-seq), a protocol designed to systematically identify potential TF-binding sites on the nucleosome. We describe steps for nucleosome library assembly, SeEN-seq assay, and cryoelectron microscopy (cryo-EM) sample preparation. This protocol facilitates the preparation of homogeneous pioneer TF-nucleosome complexes for cryo-EM structure determination using single-particle analysis. For complete details on the use and execution of this protocol, please refer to Michael et al.1.

Abstract Tropospheric ozone has the potential to become an increasingly pressing public health issue in Bogotá, Colombia, due to rising concentrations across the city driven by complex interactions among emissions, meteorology, and urban structure. This study presents a comprehensive spatiotemporal analysis of ozone levels from 2013 to 2023 and assesses the associated health burden using mortality data from the same period. Results reveal a consistent upward trend in ozone concentrations, particularly in northern, western, and southern localities, with seasonal peaks linked to biomass burning and photochemical conditions. Mortality analysis, based on the Global Exposure Mortality Model, estimates that 18.3% of all deaths among individuals aged 25 and older are attributable to long-term ozone exposure. The highest burdens are found in densely populated and socioeconomically vulnerable areas such as Kennedy, Suba, and Ciudad Bolívar, with the elderly being the most affected. Building on these findings, we developed a machine learning prediction model for ozone using a convolutional merge with a long-short term memory network architecture trained on air quality and meteorological variables. The model demonstrated strong predictive performance (mean Rho=0.86, RMSE=3.5 $$\upmu$$ g/m 3 ) across monitoring stations (17 with at least 35000 data points), supporting its potential application in real-time early warning systems across Bogotá. This integrated approach highlights the importance of localized air quality management, combining epidemiological assessment with predictive modeling. The findings underscore the urgency of implementing region-specific mitigation strategies and improving monitoring infrastructure to reduce health risks from ozone exposure in Bogotá’s rapidly growing urban environment. Graphical Abstract The top-left panel shows a rising trend in tropospheric ozone concentrations across Bogotá, highlighting the increasing severity of air pollution. The top-right panel represents the associated health burden, indicating elevated mortality risks, particularly among older and vulnerable populations. The bottom-left panel depicts the machine learning framework developed in this study, integrating a convolutional layer and a bidirectional long-short term memory architecture to predict future ozone levels with high accuracy, showing its potential as a base for early warning systems. Finally, the bottom-right panel emphasizes the study’s geographical focus on Bogotá, Colombia.

Abstract Trans-species RNA interference (tsRNAi), in which plants produce small RNAs (sRNAs) to silence target genes in pathogens, has emerged as a promising strategy for disease control. However, whether tsRNAi constitutes an endogenous, regulated immune response remains unclear. Here, we show that ARGONAUTE10 (AGO10) plays a critical role in pathogen-induced tsRNAi. Loss of AGO10 in Arabidopsis abolished pathogen gene silencing during infection, leading to hypersusceptibility to oomycete and fungal pathogens. Importantly, AGO10 rapidly responds to pathogen infection through increased protein accumulation and re-location into discrete cytoplasmic condensates, thus promoting the production of trans-species sRNAs at the pathogen infection sites. This immune responsiveness relies on the N terminal intrinsically disordered region (IDR) of AGO10, which is responsible for sensing and responding to immune activation. Specific features in the IDR partitions AGO10 into two deeply diverged subgroups, AGO10a and AGO10b, with the immune responsiveness and defense function evolutionarily conserved in AGO10a but not AGO10b. Together, these findings establish tsRNAi as a bona fide, evolutionarily conserved immune response and position AGO10 as a signal-responsive hub linking pathogen perception to tsRNAi-based defense.