bluuebunny/crossref_metadata_2025 huggingface dataset:
Pumped hydropower storage in open-pit mines can provide substantial contributions to the EU energy transition – a case study for Germany
Authors: Ernst Priscilla , Kempka Thomas
Published: March 2024

Decommissioning of lignite mines in the course of phasing-out electric power generation from fossil fuels in the European Union (EU) is one of the strategic key pillars to reduce net greenhouse gas emissions by 55% compared to the 1990 levels until 2030, and achieving climate-neutrality by 2050. Germany’s emission reduction targets are even more ambitious with 65% and 88% scheduled for 2030 and 2040, respectively.Repurposing phasing-out open-pit lignite mines into Hybrid Pumped Hydropower Storage (HPHS) installations for excess energy from the electric grid and renewable sources contributes not only to the EU Green Deal and EU energy supply security, but additionally increases the regional economic value and stabilises the job market. Pumped hydropower is well established for storing excess energy from the electric grid and for load balancing with a total installed capacity of 7.89 GW in Germany and a current total share of 78.6% in the energy storage sector. Total round-trip efficiencies of up to 85% and extraordinary high storage capacities compared to battery-based solutions can be realised. Another advantage of implementing the technology in former open-pit mines is that costs of constructing the two required storage reservoirs are significantly reduced due to the presence of the open-pit hole. Multiple open-pit lignite mines were closed in Germany in the past decades, and nine are expected to cease operation by 2038. Several studies assessing the potentials for PHS based on existing reservoirs have been undertaken, but these do not yet consider the additional potentials of open-pit mines. The aim of the present study was to investigate the potential theoretical and technical power production and storage capacities becoming available by repurposing open-pit mines into HPHS installations. For that purpose, a database of German open-pit lignite mines was established. An analytical model was employed to determine the power production and storage capacities of 34 German open-pit lignite mines, of which 13 meet the previously defined site selection criteria. The results of the present study show that the currently installed energy storage potentials in Germany can be extended by additional 1.42 GW (increase by >18%), increasing the installed PHS capacity by 22.9% at the same time. These findings are essential to guide policy and decision makers involved in the German and EU energy transition. The methodology will be extended to member states of the European Union in the next step.The present study has received funding from the Research Fund for Coal and Steel—2020, under grant agreement No. 101034022 (ATLANTIS).
EMBAL - European Monitoring of Biodiversity in Agricultural Landscapes
Authors: Kleinewillinghöfer Luca , Baier Clemens , Haub Carsten , Lindemann Dirk , Oppermann Rainer , Roggon Lars , Sutcliffe Laura , Buck Oliver
Published: March 2024

The 'European Monitoring of Biodiversity in Agricultural Landscapes' (EMBAL) is a monitoring initiative initiated by the European Commission that gathers information on the state of biodiversity in agricultural landscapes across EU member states. Developed within the EU Pollinator Monitoring Framework, EMBAL is a standardized and sample-based in-situ survey of 500x500m landscape sections (plots).EMBAL provides comprehensive data, including general information on land use and land cover at parcel level, information about landscape elements, and specific vegetation data on a transect level in grassland and arable habitats. Both the methodology and the sampling frame are harmonized with LUCAS (Land Use and Coverage Area frame Survey).Following a successful pilot in 2020, EMBAL was applied in all 27 EU member states in 2022 and 2023, surveying a total of 3,000 selected plots in both years. This extensive rollout served to gather harmonised baseline data on biodiversity across EU27 and provided a comprehensive field test of the EMBAL methodology across different European landscapes.In this contribution, we offer an overview of the EMBAL 2022 and 2023 rollout, the EMBAL survey methods and parameters and provide an outlook on the results.
Distributed Fiber Optic Sensing and Artificial Intelligence: preliminary results on the Campi Flegrei caldera unrest
Authors: Corsaro Miriana , Allegra Martina , Cannavò Flavio , Currenti Gilda , Festa Gaetano , Jousset Philippe , Pellegrino Daniele , Palazzo Simone , Prestifilippo Michele , Privitera Eugenio , Pulvirenti Mario , Ricciolino Patrizia , Spampinato Concetto , Scotto di Uccio Francesco , Tramelli Anna
Published: March 2024

Since 2005 the Campi Flegrei caldera (Southern Italy) has been experiencing a long-term unrest characterized by a recent increase in seismicity and ground uplift rate. In August and September 2023, the unrest showed a sudden intensification in the number and maximum magnitude of earthquakes, culminated with the occurrence of a Md 4.2 event. In an effort to strengthen the monitoring activity, two Distributed Acoustic Sensing (DAS) devices were connected from October 2023 to telecommunication fiber optic cables in the densely populated Campi Flegrei area. The DAS interrogators are set up inside two TELECOM central offices in the city center of Naples and in Bagnoli. Dynamic strain rate data are continuously acquired with a gauge length of 10 m at an average spatial sampling of 4 and 5 m. In this framework, an automated real-time workflow has been implemented comprising both DAS data downsampling and transferring to INGV data processing center. From the beginning of the DAS acquisition (19 October 2023), more than 300 seismic events have been recorded by the INGV local seismic network, including a Md 3.0 earthquake occurred on 23rd November 2023. Unknown cable installation conditions, poor coupling of the fiber optic cable with the ground, intense traffic and anthropogenic activities make the DAS data highly noisy and, hence, pose challenges for the application of traditional seismic data processing algorithms. In this study, we propose and apply AI based algorithms to improve and fasten earthquake detection and seismic phase picking on the large data volume associated with the high number of DAS channels.  In particular, the compared algorithms cover recently published state-of-the-art deep learning networks. We show preliminary results that demonstrate the ability of the synergy between DAS and AI to contribute to the rapid response to volcanic crises.
Trace gas detection on a photonic chip with sub-ppb detection levels and isotope discrimination 
Authors: Jágerská Jana , Seton Ragnar , Zakoldaev Roman , Salaj Jehona , Vlk Marek
Published: March 2024

Spectroscopic techniques, such as laser absorption spectroscopy (LAS), are the gold standard for trace gas detection in environmental research and atmospheric monitoring. Nevertheless, the large size and cost of the LAS instruments limit the scale and contexts in which they can be employed. A promising solution to this challenge is the development of LAS sensors based on photonic integrated circuits. However, the detection limits of the on-chip sensors remain to date in the upper ppm range, i.e. several orders of magnitude higher than that of large instruments based on bulk optics. 

Here we will present an on-chip LAS sensor based on an integrated nanophotonic waveguide capable of detecting CO2 with a record-low sensitivity of 20 ppb. This result was achieved through a careful waveguide design, aiming at (i) operation in the mid-infrared spectral range, (ii) strong evanescent field interaction between the guided light and the gas sample, and (iii) minimizing spurious etalons that are a major source of spectral noise in on-chip devices. Moreover, with this sensor we demonstrate the first δ13C isotope ratio measurement realized with an on-chip device. The achieved precision of the order of 1‰ is comparable with high-end commercial instruments.  

This work is more than an incremental step in on-chip gas sensor development; it is a leap forward for in-situ gas monitoring. Our sensors offer a scalable, efficient solution for autonomous monitoring of remote areas and sensor networks of the future. Additionally, with their minute active sensing volume (microlitre), the on-chip devices open new possibilities for applications where small sample volumes are required such as ice-core samples analysis and microbiological essays
Supraglacial lake drainage through gullies and fractures
Authors: Humbert Angelika , Helm Veit , Zeising Ole , Neckel Niklas , Salzano Robert , Esposito Giulio , Braun Matthias , Steeb Holger , Sohn Julia , Bohnen Matthias , Müller Ralf , Rückamp Martin
Published: March 2024

The mechanisms of drainage of supraglacial lakes are not yet fully understood. Here we present an indepth study of drainage characteristics of a 21km^2 large supraglacial lake in Northeast Greenland from its genesis in mid 1990s to 2023. We discuss the fracture modes involved in drainage and compare this to simulated principal stress fields. A particular focus of the presentation is the formation of gullies. Using high resolution optical satellite imagery (WV2 and Planet), we detect fracture networks at the surface. We find evidence for reactivation of former gullies in subsequent lake drainage events. In addition we present viscoelastic modelling of gullies at the surface that support the continued existence of open gullies at the surface. In vertical direction, we surveyed the glacier using airborne radio echo sounding in 2016, 2018 and 2021. This data reveals englacial channels and their remnants over the entire live span of the lake. 
Seasonal and interannual inorganic carbon dynamics in the Northeast Pacific
Authors: Franco Ana C. , Ianson Debby , Bernardello Raffaele , Monahan Adam H.
Published: March 2024

The Northeast Pacific is an important net sink of atmospheric CO2. However, substantial natural variability modulates the long-term increase of seawater partial pressure of CO2 (pCO2), potentially influencing the magnitude of the CO2 sink. In addition to the seasonal cycle, the Pacific Decadal Oscillation (PDO) is known to play a role in driving a large fraction of the non-seasonal variability in the region. Yet, the magnitude of this natural variability, especially periods of high surface dissolved inorganic carbon (DIC), are not well constrained. Here we quantify the seasonal and non-seasonal variability in DIC and pCO2 using observations from the Line P program, the longest marine carbonate system time-series transect in the NE Pacific (1990-2019), as well as an ensemble of historical simulations with an Earth system model (EC-Earth-CC). Preliminary results show that the mean amplitude of the DIC seasonal cycle is similar across our NE Pacific transect (23-30 µmol kg-1) and decreases with depth to less than 5 µmol kg-1 at 60 to 70 m. In contrast, the non-seasonal variability remains approximately constant with depth, ranging between 10 – 20 µmol kg-1. We quantify the role of the PDO in driving this residual non-seasonal variability, and analyse the contrasting impact of temperature and DIC changes in controlling surface pCO2 during opposite phases of the PDO.
Improving access to climate information: The Met Office Climate Data Portal
Authors: Sanderson Michael , Woods Alex , Marshall Catherine , Stevens Laurie , Veal Anthony , Ramsey Victoria , Hodge Katie , Mitchell Timothy , Richardson Mathew , Lowe Jason , Chapter Samuel
Published: March 2024

Across Europe, a wide variety of public and private organisations require climate hazard and impact data both to improve understanding of current and changing risks and inform adaptation measures. Climate hazard and impact data currently require considerable technical expertise to access, download and interrogate creating a barrier for policy makers, local authorities, non-governmental and citizen organisations, and other interested parties. In the UK, the Met Office, in partnership with ESRI, has created a Climate Data Portal to address this issue. It provides a selection of climate data and supporting documentation in user friendly, ready-to-use data formats. Built using ArcGIS Hub, Esri’s cloud-based data engagement platform, the portal makes it much easier for users to view climate data geospatially and analyse climate change projections alongside their own data. Datasets currently available include a selection of historical climate records, climate projections and climate change impact metrics. The authors have also been exploring ways to use the platform to provide bespoke information for different sectors, including Local Authorities which are elected bodies that provide a range of services for particular geographical areas. This presentation will include a brief demonstration of the portal.
Embracing the structural framework of soil in microbiological and ecological research
Authors: Schmidt Hannes , Nunan Naoise , Raynaud Xavier , Schlueter Steffen , Felde Vincent , Zeller-Plumhoff Berit , Marmasse Gaëlle , Canarini Alberto , Fuchslueger Lucia , Richter Andreas
Published: March 2024

Soil is a complex system with a high degree of physical, chemical, and biological heterogeneity. Soil structure is organized into entangled pore networks that provide an immense surface area and are partially filled with gases and aqueous solutions. This heterogeneous landscape houses a multitude of active and inactive organisms of which soil bacteria and fungi are considered the driving forces of nutrient cycling and biogeochemical processes. Standard analyses such as soil respiration are meaningful measures to estimate processes on a meso- and macroscale while the biological agents whose actions we measure are mainly to be found on the microscale. Yet, our understanding of microbial living conditions in soil and their consequences for activity, growth, and turnover is severely limited. In this presentation I will focus on a microbial perspective and present data from various experiments where soil microbial identity and/or activity were investigated while acknowledging spatial aspects of their microenvironments. I will provide an updated view on spatial distribution of microbial communities within soils, including evidence that suggests that the density of bacteria in soils has likely been underestimated by orders of magnitudes for decades. Microbial density arguably is a major determinant of cell-to-cell interactions, and thus many processes involved in microbial nutrient cycling that we measure on a larger scale. I will further argue to embrace soil heterogeneity rather than minimizing it, for example by using intact soil cores instead of sieved soils for stable isotope labelling experiments. I will present data on in situ bacterial and fungal growth which suggests that preserving soil physical architecture while investigating microbial parameters could bring experimental measurements significantly closer to field conditions, while also opening new avenues to improve our understanding of spatial aspects of soil microbiology.
Measures to adapt to climate change and mitigate the impact of droughts in alluvial aquifers and rivers
Authors: Epting Jannis , Affolter Kast Annette , Scheidler Stefan , Råman Vinnå Carl Love , Schilling Oliver S
Published: March 2024

As a result of climate change, periods of drought are likely to be longer and more frequent. Many small and medium-sized rivers are already drying up in summer and becoming temporary watercourses with all the limiting factors such as oxygen deficiency, excessively high temperatures and their effects on aquatic fauna in particular.One way to counteract the drying up of impacted rivers is to artificially recharge the alluvial aquifers that feed the rivers via a geoengineering method called Managed Aquifer Recharge (MAR). There are various infiltration-based methods employed in MAR, including groundwater recharge via natural and technical infiltration basins or particularly in built-up areas with injection wells that recharge the water directly into the aquifer. The enhanced exfiltration of groundwater as a result from MAR to targeted rivers is called Managed Surface Water Recharge (MSWR). By artificially raising the groundwater level using MAR from nearby rivers/basins to such an extent that the groundwater level is subsequently hydraulically higher than the river level, so that the groundwater can flow into the rivers affected by drought.To sustainably achieve MSWR, MAR should take place when surface water is abundant during medium and high river discharge periods such that groundwater later can exfiltrate into rivers during low water periods. Another positive effect of MSWR lies in the fact that low water periods and drought often occur during hotter summer months. While MAR is often optimal during the cooler snowmelt periods or rain intensive transitional seasons, resulting in comparatively lower temperature of water entering into the ground. MSWR is thus often automatically accompanied by an ecohydrologically relevant cooling effect achieved via the enhanced exfiltration of comparatively cooler groundwater during hotter summer periods. Thus, in addition to an increase in groundwater exfiltration into surface waters during drought periods, MSWR also has the benefit of ecological enhancement in rivers with respect to water temperature and quality.Here, we present first results of our current research into practical MAR-MSWR, which we have conducted at different sites in urban and rural environments in pre-Alpine Switzerland. We present process-based results for different spatial, temporal and operational scales including both local (river-reach) as well as regional (river-basin) perspectives.
Self-supervised learning for mapping rice areas reduces the need for field surveys
Authors: Narayanan Mukund , Sharma Ankit , Ilampooranan Idhayachandhiran
Published: March 2024

Traditional approaches to mapping rice areas depend on detailed field surveys to gather label data. This data gathering, while thorough, is often time-consuming, and costly. Further, for long-term rice area mapping, especially at large scales, ground validation can only be made in recent years, which may lead to uncertainties in rice areas of the past. To solve this limitation, this study introduces a novel method for classifying rice cultivation areas without the need for field data by employing a self-supervised learning framework within Google Earth Engine. Using MODIS data to calculate indices such as the Normalized Difference Vegetation Index (NDVI), the Enhance Vegetation Index (EVI), and the Land and Surface Water Index (LSWI), a pseudo-label boundary was delineated. The delineation of the boundary involved marking flooded regions, where LSWI + 0.05 ≥ NDVI EVI. Within these flooded regions, instances when EVI in the first 40 days was at least half of the peak EVI during the growing season were assigned as rice. In contrast, regions that did not meet these criteria were considered for non-rice. As proof of concept, the delineated pseudo-label boundaries of rice and non-rice were randomly sampled and trained on several machine learning models like random forests, support vector machines, gradient-boosted trees, and decision trees, to classify rice areas in Punjab, India, from 2003 to 2022. The random forest model demonstrated superior performance, achieving an Area Under the Curve of receiver-operating characteristics (AUC) of 0.71, compared to other models (AUC of ~0.55). Furthermore, comparing the self-supervised models against the same machine learning models, which were traditionally trained on field survey data (228 ground points: 164 rice, 64 non-rice was collected), the self-supervised models showed ~10% higher performance than their traditionally supervised counterparts. Therefore, this study demonstrates that using this self-supervised modeling framework reduces the need for field-based annotations, while still providing reasonably accurate rice area maps.
Day- and nighttime heatwave clusters over Europe and their physical drivers
Authors: Hansen Felicitas , Feser Frauke , Zorita Eduardo
Published: March 2024

Heatwaves rank among the most devastating extreme events over Europe, particularly in terms of mortality and agricultural damage. Heatwaves are often defined as exceedances of thresholds based on daily maximum temperatures, thus considering only daytime effects. However, exceedances of daily minimum temperatures, which often occur at night, are of similar importance, as they can cause additional stress to the human health due to shorter recovery times. For both daytime and nighttime heatwaves, knowledge of the underlying mechanisms is crucial for the successful prediction of the events; however, these mechanisms are not yet fully understood.Although heatwaves can occur quite locally, the task of heatwave prediction can be simplified by representing heatwaves as recurring large-scale patterns. The aim of the presented work is to identify these dominant coherent spatial patterns over Europe using the SANDRA (Simulated Annealing and Diversified Randomization) clustering method for both daytime and nighttime heatwaves. In a second step, relevant atmospheric, oceanic and land variables are investigated for their physical connection to each of the European heatwave clusters with different time lags.The results obtained from reanalysis data covering the recent past are compared with those obtained from a long coupled global climate model simulation of the last 2000 years, performed with the MPI-ESM model. The long model simulation is further used to place the recent record-breaking European heatwave of summer 2022 in a historical context.
Soil bioturbation – how habitat climatic constraints shape biophysical processes
Authors: Ruiz Siul , Hallett Paul , Or Dani
Published: March 2024

Soil bioturbation is the physical movement and alteration of soil by fauna and plants. It plays a central role in soil formation at long time scales and shapes soil structure at hydrologic time scales that affects many soil physical processes and ecosystem services. Varying biomes act as hosts to a variety of ‘ecosystem engineers’ that have developed unique strategies for generating habitats and appreciable soil biopores. For example, earthworms and plant roots generate biopores in moist soil via penetration-expansion (and some ingestion) processes. Other organisms such as ants or termites excavate soil by displacing soil particles with their limbs. We present an overview of biophysical processes associated with soil bioturbation driven by various biological agents and biomechanical  strategies (e.g. penetrators vs excavators). The study highlights the critical role of soil moisture and texture in modulating and shaping the various biomechanical strategies and activity time windows. We explore the implications that regional environmental factors play in locally favoring particular bioturbation processes, which can be used to identify the likelihood of a specific bioturbation agents occurrence in a given region. The overview highlights the relative impact of soil bioturbation on soil structure formation by comparison with conventional tillage processes (restricted to arable lands). Ultimately, this work endeavors to open discussions that can aid in reducing intensive mechanized tillage and help guide sustainable land use initiatives by capitalizing on the biological agents present in the environment.   
Contaminant Source Identification via Transfer Learning on Multifidelity-Data.
Authors: Chiofalo Alessia , Ciriello Valentina , Tartakovsky Daniel M.
Published: March 2024

Reconstruction of contaminant release history is crucial for subsurface remediation actions. This task amounts to a high-dimensional inverse problem, whose solution requires multiple forward solves of contaminant transport equations. It also must cope with both sparse observations of solute concentration and subsurface heterogeneity. The computational burden of solving this inverse problem can be reduced by deploying a surrogate model, e.g., neural networks (NNs), which provides a low-cost approximation of its expensive physics-based counterpart. However, to construct such NNs, a large amount of high-fidelity forward runs may be required to provide training data, and these computations might be as cost-prohibitive as the solution of the inverse problem. To address this issue, we generate multi-fidelity data by running simulations of the forward transport model on fine and coarse meshes. The resulting high- and low-fidelity temporal snapshots of solute concentration are subsequently used, with a Transfer Learning technique, to train a Convolutional NN to identify the initial contaminant source location. The training is divided into three phases. In the initial phase, the training exclusively employs low-fidelity data. In the subsequent two steps, the learning phase for the network is finalized with only a relatively small number of high-fidelity data. The obtained results demonstrate that the transfer-learning-based surrogate model is a promising tool to reduce the computational cost as well as to obtain accurate solutions of high dimensional inverse problems.
Segment Every Fossil - A Deep Learning Model Tailored for Automatic Segmentation of Microfossils in Thin Section Images
Authors: Ferreira-Chacua Ivan , Koeshidayatullah Ardiansyah
Published: March 2024

Microfossil segmentation and identification, an integral step to paleontological and geological research, has historically required laborious manual analysis for segmentation due to their intricate structures and non-trivial segmentation through traditional Machine Learning and Computer Vision techniques. For tackling this, we introduce "Segment Every Fossil" (SEF), a deep learning model specifically designed for the automatic segmentation of a diverse array of microfossils specimens in thin section images. SEF builds upon established foundation models such as the Segment Anything Model (SAM) and Segment Every Grain, enhancing their capabilities through custom U-Net architectures and attention mechanisms. This meticulous development, supported by a vast dataset of over 8,000 labeled images, propels SEF to achieve an impressive 0.91 accuracy and 0.93 loss, surpassing traditional segmentation methods. This is accomplished through a dual-stage segmentation process that utilizes the custom U-Net for initial segmentation, followed by the Segment Anything Model for precise refinement. This ensures accurate delineation of overall fossil shapes and captures the morphology of the specimen at hand. SEF's custom loss function and U-Net architecture heighten its ability to discern subtle fossil boundaries, a crucial factor for precise analysis, while attention mechanisms guide SEF to prioritize and analyze critical regions within microfossils, enabling detailed morphometric measurements of features such as size, area, and grain-matrix ratios.
SEF's performance unlocks possibilities for quantitative microfossil analysis, enabling accelerated research workflows and consequently faster and deeper insights into fossil diversity and evolution. With ongoing efforts to reduce dataset size requirements and explore weakly supervised and self-supervised learning techniques, we aim to enhance SEF's capabilities by exploring the reduction of the dataset size required and make automated microfossil analysis accessible to broader research communities with less data available for training Deep Learning models. SEF represents a significant leap forward in microfossil analysis, offering an accurate, efficient, and being a potentially transformative model for micropaleontological discoveries, holding promise for reshaping the landscape of paleontological and geological research, ushering in a new era of automated and accessible microfossil analysis.
NASA's FIRMS: Enabling the Use of Earth System Science Data for Wildfire Management
Authors: Olsina Otmar , Hewson Jennifer , Davies Diane , Radov Asen , Quayle Brad , Giglio Louis , Hall Joanne
Published: March 2024

NASA’s Fire Information for Resource Management System (FIRMS) enables users to find and analyze a range of earth system science data and information relevant to the complex and evolving field of wildfire management, impacts, and mitigation. FIRMS facilitates the use of earth system science data to inform science-based decision making through a standardized, readily interpretable interface that supports operational users, researchers, and non-scientific stakeholders. This community-driven interface enables user-friendly exploration of data that are increasingly findable, accessible, interoperable, and reproducible (FAIR), and the interface is regularly refined to support the diversity, equity, and inclusion of potential end-users. FIRMS offers fire-based maps through Web Map Service (WMS) and Web Feature Service (WFS), and makes available multiple APIs to support area, country, fire footprint features for stakeholders needing to ingest data into software such as QGIS, ArcGIS, etc. FIRMS developers are also creating a Fire Data Academy to build capacity around the use of Jupyter notebooks, Google Colab, and Python to perform data ingest, manipulation, and visualization. As the impacts of wildfires expand, affecting increasing swaths of population and biodiversity through immediate infrastructure and habitat destruction, and causing longer-term air quality impacts, a transdisciplinary approach to research and response is required. FIRMS supports a transdisciplinary approach through the range of data and information available, ensuring that all users, including those in historically underrepresented communities, can access wildfire data.
Go or grow? An interdisciplinary ‘biogeomorphic balance’ concept linking moving mountain slopes and shifting mountain plants
Authors: Eichel Jana , Stoffel Markus , Wipf Sonja
Published: March 2024

High mountains are hotspots of climate and global environmental change. Mountain biodiversity is threatened by quickly rising temperatures which cause vegetation shifts, such as upslope migration. At the same time, natural hazards develop as mountain slopes become increasingly unstable due to permafrost degradation and changes in rain and snowfall regimes. Resulting slope movements, such as rockfalls and debris flows, can limit colonization by plants. However, plants that manage to colonize mountain slopes can stabilize them through their roots and above ground biomass.Therefore, we believe that an interdisciplinary approach linking ecology and geomorphology is needed as a next step to better understand how climate change affects high mountain landscapes and ecosystems. Combining results from previous geomorphic, ecological and palaeoecological studies, we show that the response of high mountain environments to climate change can depend on the balance between slope movement intensity and the trait-dependent ability of plants to colonize and stabilize moving slopes. For this ‘biogeomorphic balance’ we envisage three possible scenarios: (1) Intensifying slope movements impede vegetation shifts, amplifying instability. (2) Ecosystem engineer species, adapted to moving slopes, stabilize slopes and facilitate shifts for less movement-adapted species. (3) Competitive trees and tall shrubs, shifting on stable slopes, reduce instability but potentially diminish biodiversity. Given the disparate rates of ecological and geomorphic responses to climate change, coupled with high environmental heterogeneity and elevational gradients in in mountains, we anticipate that future biogeomorphic balances will be variable and heterogeneous in both space and time.To unravel these intricate biogeomorphic balances, we advocate for collaborative research between mountain geomorphologists and ecologists and propose three distinct future directions that combine advancing field measurement, remote sensing techniques and modeling approaches. We believe that by recognizing high mountains as 'biogeomorphic ecosystems', shaped by the interplay of geomorphic and ecological processes, we can improve our ability to safeguard people, infrastructure and ecosystems in mountain environments around the world. References:Eichel J, Stoffel M, Wipf S. 2023. Go or grow? Feedbacks between moving slopes and shifting plants in high mountain environments. Progress in Physical Geography: Earth and Environment 47 : 967–985. DOI: 10.1177/03091333231193844
Modeling mechanical stress in freezing soils: sub-Arctic infrastructure, built environment and frost quakes
Authors: Remes Jere , Okkonen Jarkko
Published: March 2024

The modeling of the coupled thermo-hydro-mechanical processes within a soil undergoing freeze-thaw cycles is an increasingly relevant problem in the era of accelerating climate change. One can hope to alleviate and prepare for infrastructure damages due to e.g., frost heave and frost quakes by modeling the interplay of hydrology and soil mechanics and identifying at-risk structures and environmental profiles (i.e. temperature gradient, snow cover, soil water/ice saturation) that make those structures susceptible to said damages.Our work is focused on developing a linked computational framework for thermo-hydro-mechanical modeling of soils. We achieve this currently by linking the state-of-the-art thermo-hydrological modeling of Amanzi-ATS with the thermo-mechanical modeling capabilities of OpenGeoSys, allowing us to have an accurate understanding of both the intricate hydrology of freezing soils as well as being able to determine the stress and pressure fields within the system. This framework is then to be applied to understand the mechanics and triggering circumstances at the frost quake site at Talvikangas in Oulu, Finland as well as developing a risk-assessment tool for damages to infrastructure and built environment.
Routing climate model runoff from CMIP6 to project future changes in global river discharge
Authors: Seubert Pauline , Thober Stephan , Schumacher Dominik , Seneviratne Sonia I. , Gudmundsson Lukas
Published: March 2024

Hydrological extreme events are expected to change in frequency and magnitude due to anthropogenic climate change. Climate impact studies investigating effects on floods and hydrological droughts require knowledge on discharge along river networks. However, global climate models (GCMs) focus on runoff at the grid cell level, have only a coarse resolution, and typically address runoff routing externally. To bridge this gap, atmospheric data (e.g., precipitation, temperature) from GCMs are fed into global hydrology models (GHMs). While this approach can benefit from the additional detail of GHMs, which are dedicated to resolve the terrestrial water balance, it can only consider a limited number of GCM projections. This implies that the substantial spread imposed by both GCM uncertainty and internal climate variability may be underestimated. To overcome this limitation, we route daily runoff from multiple models contributing to the 6th phase of the Coupled Model Intercomparison Project (CMIP6) along the river network. For this we use the multiscale routing model mRM which can be flexibly adapted on a range of spatial scales by deriving an upscaled river network from high-resolution morphological data. The fidelity of the considered modelling chain is carefully evaluated in light of the underlying assumptions and the scale mismatch between the spatial resolution of the GCMs and the routing model. The new global discharge projections are used to explore the effects of anthropogenic climate change on mean and extreme river flow considering the uncertainty imposed by models contributing to the CMIP6 archive.
Understanding Astronomically Forced Carbon Cycle Feedbacks Through the Lens of an Earth System Model 
Authors: Vervoort Pam , Kirtland Turner Sandra , Hulse Dominik , Greene Sarah , Ridgwell Andy
Published: March 2024

Milankovitch cycles recorded in marine sediments demonstrate the influence of astronomical forcing on Earth’s climate-carbon dynamics. Proxies suggest that during greenhouse climates, isotopically light carbon is released during episodic warm intervals (at eccentricity maxima) and re-sequestered during the following cooling (at eccentricity minima). However, the dominant carbon sources and sinks at play on orbital timescales remain unclear-- particularly when large dynamic ice sheets are absent as during the early Cenozoic. Methods: In an Earth system model (ESM), we apply 4-Myr-long transient astronomical forcing to examine how various climate-sensitive physical and (bio)geochemical processes respond and how this forcing is recorded in key oceanographic variables (temperature, pCO2, δ13C of DIC, and wt% CaCO3). Among others, we assess the impact of marine productivity, CaCO3 compensation, terrestrial weathering, organic matter burial, and phosphorus cycling. Results: Most processes are driven by changes in local conditions -controlled by obliquity and precession, but these high-frequency changes are converted to low-frequency eccentricity cycles expressed in pCO2, benthic δ13C, and wt% CaCO3 as a result of the lowpass filtering effect of the ocean reservoir. While the magnitude of early Cenozoic δ13C variability can be explained by astronomically forced input and burial fluxes of marine organic carbon alone, the dominant frequency and relative phasing of proxies highly depend on the geographic distribution of landmasses that control organic carbon fluxes. For example, only short eccentricity cycles of 100 kyr periodicity (as opposed to long 400 kyr cycles) are simulated in benthic δ13C under favorable paleogeographic configurations. In our model, the pCO2 and temperature response to orbital forcing is minimal, and eccentricity maxima coincide with enhanced preservation of CaCO3. In contrast, early Cenozoic proxies suggest a stronger temperature response and reduced CaCO3 preservation during warm intervals. Implication: Our results support the hypothesis that additional feedbacks that are not yet included here (e.g., terrestrial carbon or methane) were likely important controls during orbital-scale climate variability in greenhouse climates.
The Attenuation and Scattering Signature of Fluid Reservoirs and Tectonic Interactions in the Central-Southern Apennines (Italy) 
Authors: Talone Donato , De Siena Luca , Lavecchia Giusy , de Nardis Rita
Published: March 2024

The intricate tectonics of Central-Southern Italy, characterized by its complex fault network and sparse seismicity distribution, have posed a significant challenge to understanding the region's seismic hazard, its three-dimensional structural assessment, and the role of fluids in the seismic release. Conventional geophysical techniques, often limited by low seismicity rates, have struggled to provide a comprehensive picture of the crustal structures, and a coherent geophysical model of the area is still absent. Leveraging the last decade’s expanded detection capabilities of the Italian seismic network, we were able to make up for this lack and employed seismic attenuation and scattering tomography methods to produce complete 3D attenuation models of the crust.By analyzing the energy loss of seismic waves as their propagation through the crust, the study revealed a pervasive pattern of high attenuation zones that extend along the entire Apenninic Chain, particularly concentrated in Southern Italy. The distribution of these anomalies aligns closely with the regional fault structures suggesting a strict relationship with the fracture level due to the tectonic processes. In contrast to the bigger anomalies, the study also identified prominent low attenuation and scattering volumes corresponding to the Fucino and Morrone-Porrara fault systems. These are likely regions of accumulated stress where the locked seismic energy release contributes to the high seismic hazard. Furthermore, the study identified a previously undetected high-attenuation region beneath the Matese extensional system, indicating a potential source of both deep and shallow circulation of fluid. Another anomaly was detected near the L'Aquila 2009 seismogenic area, suggesting a regional distribution of fluid-rich areas.The findings of this study provide unprecedented insights into the tectonic interactions and fluid sources of Central-Southern Italy, with significant implications for seismic hazard assessment, fluid exploration, and the development of effective mitigation strategies for this geologically active region.
Mean, Seasonal Cycle, Trends, and Storage of the Southern Ocean carbon cycle in the RECCAP2 assessment (1985-2018)
Authors: Patara Lavinia , Hauck Judith , Gregor Luke , Nissen Cara , Hague Mark , Mongwe Precious , Bushinsky Seth , Doney Scott C. , Gruber Nicolas , Le Quéré Corinne , Manizza Manfredi , Mazloff Matthew , Monteiro Pedro M. S.
Published: March 2024

The Southern Ocean has long been known to be an important region for ocean CO2 uptake, and one which is especially sensitive to changes in the overlying climate. Here we assess the Southern Ocean CO2 uptake (1985–2018) using data sets gathered in the REgional Carbon Cycle Assessment and Processes Project Phase 2 (RECCAP2). These include global ocean biogeochemical models (GOBMs), surface ocean pCO2-products, data-assimilated models, and interior ocean biogeochemical observations. Over this period the Southern Ocean acted as a sink for CO2, with magnitudes which are roughly half of those reported by RECCAP1 for the same region and timeframe. Close agreement is found between GOBMs and pCO2-products, partly due to some compensation of seasonal and regional differences. Seasonal analyses revealed agreement in driving processes in winter (with uncertainty in the magnitude of outgassing), whereas discrepancies are more fundamental in summer, when GOBMs exhibit difficulties in simulating the balance of non-thermal processes of biology and mixing/circulation. The data sets emphasize strong latitudinal variations in the mean and seasonality of the CO2 flux and asymmetries in the mean and amplitude of the CO2 flux between Atlantic, Pacific and Indian sectors. The present-day net uptake is to first order a response to rising atmospheric CO2. This drives large amounts of anthropogenic CO2 (Cant) into the ocean, thereby overcompensating the loss of natural CO2 to the atmosphere driven by the changing climate. The GOBMs show, however, a 20% spread and an overall underestimate of Cant storage in the ocean interior. An apparent knowledge gap is the increase of the sink since 2000, with pCO2-products suggesting a growth that is more than twice as strong and uncertain as that of GOBMs. This is despite nearly identical pCO2 trends in GOBMs and pCO2-products when both products are compared only at the locations where pCO2 was measured.
Uncertainty of heat wave metric calculations
Authors: Skrynyk Oleg , Aguilar Enric , Cimolai Caterina
Published: March 2024

Climate/weather extremes such as heat waves (HWs) are of the great interest to study as they have the significant harmful effect on the environment and society. There are many researches dealing with the calculation of HW metrics and their long-term trends on both the global and regional/national spatial scale. In our work based on a case study of Ukraine, we aimed to quantify the uncertainty of HW metric calculations, which might originate from climate input data. To this end, we used a mini statistical ensemble of several gridded data sets of maximum daily air temperature (TX), covering the territory of Ukraine for the period of 1950-2020 (70 years) with the same spatial resolution. The ensemble included ERA5 reanalysis data (remapped by means of the CDO software to the finer grid of 0.1ox0.1o with different interpolation algorithms), ERA5-Land, E-OBS (the ensemble mean) and Ukrainian gridded observation data previously developed for the period of 1946-2020. We defined a HW as an event when conditions (TX in our case) above criteria (90-th percentile calculated based on the WMO standard 1961-1990 reference period) persist at least three consecutive days, with permission of a 1-day time gap. Four HW metrics were considered, namely heat wave number (HWN), duration (HWD), frequency (HWF) and amplitude (HWA). The calculation of yearly time series of the HW metrics was performed by means of the R package heatwaveR for each grid point of the domain and each member of the constructed statistical ensemble. The uncertainty of the HW metrics was defined as a difference between min and max metric’s values calculated for different members of the ensemble. We also calculated the range of the possible variations in long term trends of obtained yearly time series of the HW metrics. Our results showed that depending on climate data used for HW climatology analysis, the calculation results might differ significantly for a particular grid point and year. However, on average (over the whole domain and the period under study), variation of the HW metrics is not so pronounced. Moderate variations are also observed in long-term trends of the metric time series. This work has received funding through the MSCA4Ukraine project, which is funded by the European Union
NO and O3 mixing ratios above the canopy in the rainforest
Authors: Monteiro Carolina , Tsokankunku Anywhere , Harder Hartwig , Wolff Stefan
Published: March 2024

Nitrogen oxides (NOx = NO and NO2) are chemical compounds that affect and control the abundance of ozone (O3) and hydroxyl radicals (OHx = OH and HO2), the main oxidizing agents in the atmosphere. In pristine environments, these oxidizers react with biogenic volatile organic compounds (BVOCs), such as isoprenes, to produce oxidized secondary organic products. Further reaction with NOx leads to the formation of nitrates. Nitrates deposit on surfaces and grow aerosol particles which eventually act as cloud condensation nuclei. This makes NOx an important atmospheric component, even in low concentrations. Therefore, NOx measurements are being made at the Amazon Tall Tower Observatory (ATTO) research site in the central Amazon forest basin, a pristine region.Here we present the measurements of NO, O3 and meteorological parameters collected at the Walk-up tower, at a height of approximately 40 m, just above the canopy.  
Investigating Arctic Clouds and Water Vapor over Sea Ice: Airborne Passive Microwave Observations during HALO-(AC)3
Authors: Risse Nils , Mech Mario , Prigent Catherine , Crewell Susanne
Published: March 2024

Clouds and water vapor play a critical role in the water and energy balance of the Arctic. However, few field observations of these quantities over sea ice exist. Passive microwave observations provide high sensitivity to clouds and water vapor with high spatial and temporal coverage in polar regions. However, retrievals of atmospheric quantities from satellites and aircraft require a description of the variable sea ice emissivity, which depends on the properties of sea ice and snow. Recently, improved retrieval methods that derive sea ice and atmospheric properties simultaneously allowed for improved exploitation of the information from passive microwave observations.This work presents liquid water path (LWP), ice water path (IWP), and integrated water vapor (IWV) retrieved from the HALO Microwave Package (HAMP) operated onboard the HALO aircraft during the HALO-(AC)3 field campaign in spring 2022 in the Fram Strait. The nadir-viewing HAMP measures along two water vapor bands (22.24 and 183.31 GHz), two oxygen bands (50-60 and 118.75 GHz), and the atmospheric windows at 31 and 90 GHz over different surface types. The retrieval accounts for variable surface emission through a joint surface-atmosphere optimal estimation scheme with the Passive and Active Microwave Radiative Transfer (PAMTRA) model.The high spatial coverage of the HALO flights allows for assessing the spatial and temporal variability of the retrieved IWV, LWP, and IWP under various atmospheric and surface conditions. A particular focus lies on the warm air intrusion events and their related poleward changes in cloud properties and water vapor over sea ice that HALO captured. Furthermore, the hectometer-scale airborne observations allow statistical comparison with operational satellite products, reanalysis, and model simulations along the flight track. The HAMP observations will improve the characterization of clouds and water vapor in the Arctic and potentially improve the use of passive microwave satellite observations over sea ice.