bluuebunny/crossref_metadata_2025 huggingface dataset:
Explainable AI for GNSS Reflectometry: Investigating Feature Importance for Ocean Wind Speed Estimation
Authors: Xiao Tianqi , Asgarimehr Milad , Arnold Caroline , Zhao Daixin , Mou Lichao , Wickert Jens
Published: March 2024

Spaceborne GNSS Reflectometry (GNSS-R) is a novel remote sensing technique providing accumulating data volume with global coverage and enhanced temporal resolution. The reflected pre-existing L-Band signal of opportunity transmitted by the Global Navigation Satellite System contains information about the reflection surface properties which can be quantified and converted into data products for further studies. To retrieve such information, Artificial intelligence (AI) models are implemented to estimate geophysical parameters based on the GNSS-R observations. With more and more complex deep learning models being proposed and more and more input features being considered, understanding the decision-making process of the models and the contributions of the input features becomes as important as enhancing the model output accuracy.This study explores the potential of the Explainable AI (XAI) to decode complex deep learning models for ocean surface wind speed estimation trained by the Cyclone GNSS (CYGNSS) observations. The input feature importance is evaluated by applying the SHAP (SHapley Additive exPlanations) Gradient Explainer to the model on an unseen dataset. By analyzing the SHAP value of each input feature, we find that in addition to the two known parameters that are used in the operational wind speed retrieval algorithm, other scientific and technical ancillary parameters, such as the orientation of the satellite and the signal power information are also useful for the model.We seek to offer a better understanding of the deep learning models for estimating ocean wind speed using GNSS-R data and explore the potential features for more accurate retrieval. In addition to building an efficient model with effective inputs, XAI also helps us to discover the important factors found by models which can enhance the physical understanding of the GNSS-R mechanism.
It's Not Easy Being Green: Quantitative Modeling of STEVE's Picket Fence Emissions Driven by Local Parallel Electric Fields
Authors: Gasque Claire , Janalizadeh Reza , Harding Brian , Gillies Megan , Yonker Justin
Published: March 2024

The vibrant green streaks of the 'picket fence' typically appear below a STEVE arc in the subauroral sky, at lower latitudes than the auroral oval. Recent studies suggest that, despite its aurora-like appearance, the picket fence may not be driven by magnetospheric particle precipitation but instead by local electric fields parallel to Earth's magnetic field. In this study, we investigate this hypothesis by quantitatively comparing observed picket fence optical spectra with emissions generated in a kinetic model driven by parallel electric fields in a realistic neutral atmosphere. We find that sufficiently large parallel electric fields can reproduce the observed ratio of N2 first positive to oxygen green line emissions, without producing N2+ first negative emissions. We find that, at a typical picket fence altitude of 110 km, parallel electric fields between 40 and 70 Td (~80 to 150 mV/m at 110 km) result in calculated spectral features consistent with observed ones, providing a benchmark for future observational and modeling studies. Additionally, we review studies which have identified similar features to the picket fence in the aurora, suggesting that a similar mechanism may be at work there. Since visible and ultraviolet auroral emissions are increasingly used to infer magnetospheric activity, it is important to better understand and quantify potential sources of emission beyond particle precipitation.
Influence of soil depth on global high-resolution LISFLOOD model experiments
Authors: Jensen Laura , Dill Robert , Haas Julian , Dobslaw Henryk , Grimaldi Stefania , Salamon Peter
Published: March 2024

The global hydrological model LISFLOOD (https://ec-jrc.github.io/lisflood/) is in operational use for, e.g., the Global Flood Awareness System (GloFAS) of the Copernicus Emergency Management Service. Due to its continuous development and open source availability, it is also a valuable tool for other geoscientific applications, like the assessment of terrestrial water storage (TWS) variations that can be also observed with geodetic techniques. Since TWS is understood as the sum of all hydrological storages from the surface to the deepest aquifers, it is sensitive to various aspects of the terrestrial water cycle, including surface water dynamics, soil infiltration, and groundwater flow. The current global configuration of LISFLOOD (GloFAS v4.0) has a spatial resolution of 0.05° (~5km), and utilizes a set of implementation maps that is based on various remote sensing products describing morphological conditions, soil physics, and land use characteristics.Here we investigate the influence of the soil depth parameterization on the LISFLOOD model results. We perform different model runs (for the time period 2000 – 2022) by exchanging the input soil depth map, and evaluate modeled discharge and TWS on different time scales (long-term trend, interannual and subseasonal signal) against observations. As a reference for TWS we use satellite gravimetry data from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO), which provides monthly global maps of TWS since 2002. Due to the relatively coarse resolution of the GRACE/-FO observation method, we perform the comparison at basin scale for some of the World’s largest river basins. Discharge is compared with data from gauging stations at the corresponding model grid cells.Results indicate an overall good match between modelled and satellite based TWS. Furthermore, we demonstrate the significant impact of soil depth on TWS simulations. When running the model with the standard soil map, long-term trends and interannual signals deviate from observations more strongly compared to using an adjusted soil map which is limited by the water table depth. Such findings may be valuable also for the parameterization of other hydrological models.
Utilization of hydrolyzed feather meal and chicken manure treated with micro-thermal method as recycled organic fertilizer
Authors: Kivelä Jukka , Lindegren Henrik , Niemelä Pirjo , Tuomola Mika
Published: March 2024

 
The by-products of egg production are one of the most usable raw materials for organic fertilizer. Currently they are not used as effective fertilizers. The most of chicken manure is used as fertilizer on egg production farms in combination with mineral fertilizers. Such utilization of by-products is causing significant local environmental problems. The feathers are delivered to rendering companies which are in many cases mixing them with other animal by-products. However, then it is not possible to get optimal benefit from this high value protein.There have been several projects focusing on recycling of food industry by-products. We have processed chicken manure (Fertilex Ltd) with micro thermal method developed by DTS Finland Ltd. At the first time we got chicken manure with about 4 % total nitrogen but in later trials it was possible to get a product which contained 6-7 % total N (Canasta project 2021-2023).In another project (Honkajoki Ltd) feather meal was hydrolyzed and it contained 14,2 % of total nitrogen. Previous studies have shown that 90 % of feather dry weight consists of crude keratin protein, and feathers contain about 15 % total N (Papadopoulos et al., 1985, 1986). Keratin N is not usable as nitrogen fertilizer because keratin is a stable protein.Pot trial in research greenhouseWe prepared a pot trial to find out what kind of fertilizers these developed products are. The treatments were hydrolyzed feather meal, meat bone meal (MBM), chicken manure pellets, DTS chicken manure and mineral fertilizer as control fertilizer. Hydrolyzed feather meal gave 80 % yield of mineral fertilizer which was as good as with MBM. The yield of DTS chicken manure was 5 % less compared to MBM but it was better than pelletized chicken manure.In this trial we were not able to use mixture of DTS chicken manure and hydrolyzed feather meal but if it is possible to make this kind of product. It would be suitable fertilizer at least for organic farming. Consequently, recycling of egg production nutrients becomes much more efficient.These projects were funded by the Programme for nutrient recycling of the Ministries of Agriculture and Forestry and Environment in Finland as well as Honkajoki Ltd. How to cite: Kivelä, J. et al: Utilization of hydrolyzed feather meal and chicken manure treated with micro-thermal method as recycled organic fertilizer, EGU General Assembly 2024, Vienna, Austria, 14–19 April 2024, EGU24-,
Ultraviolet radiation as a proxy measurement for electrically generated hydroxyl radicals
Authors: Jenkins Jena , Brune William
Published: March 2024

Reaction with hydroxyl radical (OH) initiates the removal of many pollutants from the atmosphere that impact human health and climate, but can also lead to the formation of different pollutants. Extreme amounts of OH are directly produced by lightning and other, weaker electrical discharges in the atmosphere, although estimates of the global impact of this source of OH are highly uncertain due to the limited field data. However, obtaining more field data is difficult, as measuring electrically generated OH with traditional OH-detecting instruments risks exposing both the instrument and the user to dangerous electrical currents. A possible alternative approach is to use the ultraviolet (UV) radiation generated by the electrical discharges as a proxy measurement for OH generation. Using a laboratory setup, the relationship between OH and UV radiation in different types of electrical discharges is investigated and quantified as a first step toward designing an instrument that can be safely deployed around electrical discharges in the field, leading to more certain estimates of the global impact of electrically generated OH.
Investigating the seasonal origin of soil and xylem water in Italian mountain catchments
Authors: Zuecco Giulia , Gentile Alessio , Nasta Paolo , Brighenti Stefano , Allocca Carolina , Bertoldi Giacomo , Canone Davide , Comiti Francesco , Fabiani Ginevra , Ferraris Stefano , Manca di Villahermosa Francesca , Marchina Chiara , Marzaioli Fabio , Penna Daniele , Previati Maurizio , Romano Nunzio , Stellato Luisa , Todini-Zicavo Diego , Borga Marco
Published: March 2024

Stable isotopes of hydrogen and oxygen are useful tracers for investigating the water sources exploited by plants for transpiration. Recent studies focused on the analysis of the temporal origin of soil and xylem water, and showed that many plant species, during the growing season, tend to take up water originating from winter rainfall events. However, the climate and biophysical factors controlling the temporal origin of soil and xylem water are still unclear. Given this background, this study aims to i) evaluate the seasonal origin of soil and xylem water by using the so-called Seasonal Origin Index (SOI), and ii) investigate the climate drivers and biophysical controls influencing the seasonal origin of xylem water.For this, we used isotopic data in precipitation, soil and xylem water along an Italian climate and topographic transect, including four Alpine and pre-Alpine catchments (Dora del Nivolet, Grangia dell'Alpe, Matsch/Mazia, and Ressi) in northern Italy, and two Apennine catchments in central (Re della Pietra) and southern Italy (Gorga). Soil samples at different depths up to a maximum depth of 1 m and vegetation samples were collected fortnightly during the growing seasons in the period 2020-2022, whereas bulk precipitation was collected fortnightly or monthly throughout the year. Vegetation samples included lignified twigs or wooden cores from different tree species (i.e., beech, chestnut, and larch) as well as roots from shrub species typical of Alpine grasslands. The dual-isotope plot evidenced a marked isotopic variability in water samples among the different sites, particularly for precipitation. Soil water reflected the seasonal variability observed in precipitation in all study sites, although the isotopic signal was affected by evaporative processes, particularly marked in the shallow soil layers. The isotopic composition of xylem water showed that in most study areas these samples had predominantly a spring and summer origin, except for soil and xylem water in the two Apennine catchments which had a strong winter (Re della Pietra) and summer origin (Gorga). So far, no clear relations between the average SOI for soil water and xylem water and climatic indicators (e.g., rainfall seasonal index) were detected. Further analyses will include other indicators which better characterize vegetation characteristics, soil properties, and terrain features. KeywordsXylem water, soil water, stable isotopes of hydrogen and oxygen, seasonal origin index
Evaluating the PolarRES regional models using tailor-made climate indices for Arctic reindeer herding communities
Authors: Matthes Heidrun , Eronen Jussi T , Fettweis Xavier , Forbes Bruce C , Gilbert Ella , Habeck Joachim Otto , Horstkotte Tim , Istomin Kirill , Komu Teresa , Landgren Oskar , Landwehrs Jan , Laptander Roza , Mooney Priscilla , Mottram Ruth , van Dalum Christiaan , van den Berg Willem Jan , Rasmus Sirpa , Rinke Annette , Tømmervik Hans
Published: March 2024

From a combination of the operational system of reindeer herding and meteorological seasonality, we developed a range of climate indices reflecting critical events in the reindeer herding year that influence the success of this livelihood. These critical events can be described as combinations of specific meteorological conditions, and therefor rendered as equations we can compute from climate model output, creating a capability for analysing different projections of the future and delivering relevant information on climate change to reindeer herding communities.For this purpose, we can use a wealth of different global and regional climate projections, with distinct advantages and disadvantages (e.g. model resolution, different greenhouse gas futures, high number of models in the ensemble for uncertainty estimates, process representation, availability of variables). For example, the CMIP6 ensemble enables the analysis of a broad range of greenhouse gas futures from a wide variety of models, allowing us to assess scenario uncertainty, but it is limited by its coarse spatial resolution. On the other hand, the PolarRES ensemble has a higher spatial resolution but is only available for one RCP/greenhouse gas future. The PolarRES ensemble consists of regional climate simulations generated by multiple regional climate models that dynamically downscale CMIP6 global climate simulations selected using a novel storyline approach. Both ensembles provide hindcast simulations that allow us to evaluate the ensemble performance with regard to the climate indices we defined.This study uses these simulations to evaluate and compare model performance to understand the potential and limitations of future projections of specific climate indices relevant for reindeer herding. We use in-situ based observations from the data set Global Summary of the Day to evaluate onset and end of the continuous freezing period, hot summer days, thawing days in autumn and freeze-thaw cycles in both spring and autumn.
Implications of the interactions between climate change, natural disturbances, and management for forest dynamics in Switzerland
Authors: Costa Maximiliano , Bugmann Harald
Published: March 2024

The Horizon Europe project “eco2adapt” aims to provide support to stakeholders, forest owners and practitioners for managing forests and optimise social and environmental resilience using the concept of nature-based solutions. The project relies on a network of study cases (“Living Labs”), for which distinct management objectives are set. The overarching research question is to identify suitable management strategies to face climate change and novel disturbance regimes. In all Living Labs, the model LandClim will be used to evaluate different management approaches and their outcomes. LandClim is a spatially explicit, dynamic vegetation model that simulates the interactions between climate, natural disturbances and forest management at the landscape scale.The Swiss Living Lab is located in the Surselva valley, in the canton of Graubünden. It represents the Alpine environment, featuring a wide range of elevations and thus biogeographical zones. Particular attention is dedicated to the protective function of forests against natural hazards (e.g., rockfall and avalanches), since it is the most particular feature of this Living Lab. Sustainable timber production is a key ecosystem service that is investigated as well, especially in the different climate change scenarios. The ultimate goal for the Swiss Living Lab is to identify how different management practices affect the sustainable provision of timber and the protective function of forests in future climate scenarios.To achieve this goal, we are working with different climate change scenarios (RCP 4.5 and RCP 8.5) as well as with a no management scenario, in order to evaluate the potential natural vegetation of the study area, and with multiple management scenarios that are co-created with local stakeholders. The protective efficiency of future stands is evaluated using specific indices for rockfall protection and avalanche mitigation. Simulation results are discussed with stakeholders in order to determine the management scenario to use for the final outputs. The final simulation results are made available to stakeholders so as to support the development of management guidelines and best practices.
Sediment ponds; an effective mitigation measure for reducing nutrient export in agricultural drainage ditches? 
Authors: Heerey Linda , Fenton Owen , Regan Fiona , White Blánaid , Kent Nigel , Daly Karen
Published: March 2024

The agricultural sector is a large contributor to poor water quality in our freshwater systems. One potential pathway for agriculturally sourced pollution to enter the freshwater environment is through drainage ditches, which can be either open surface drains or subsurface pipes, or a combination of both. While large scale tile-drainage systems with a central output point are common in some countries, Irish agricultural drainage networks tend to be comprised of a complex network of drains, each with varying levels of connectivity to freshwater systems. Recent research by Moloney et al. (2020) categorised these drains in terms of their connectivity, finding that those with a direct connection between a farmyard and river/stream were at the greatest risk for transporting the highest concentrations of nutrients (phosphorus and nitrogen). Therefore, to target the most ideal location to mitigate against nutrient transport through drainage ditches, drains with direct farmyard connectivity provide the most resource- and cost-effective option.This study investigated the effectiveness of sediment ponds installed in drainage ditches which had a direct connection between a farmyard and a river. Three case study farms were selected, two in the south (Cork) and one in the south-east (Wexford) of Ireland. All ponds were installed by 2021, with sampling commencing in December 2022. Grab water samples were collected weekly (Wexford farm) and fortnightly (Cork farms) at multiple points upstream and downstream of the ponds, and were analysed for nitrogen, phosphorus and dissolved organic carbon. Sediment samples were extracted from within the drainage ditches in summer 2023 and analysed for Mehlich-3 P, pH, Morgan’s P and particle size distribution. Initial results suggest ponds provide limited attenuation of nutrients, with no significant decreases at the downstream sample points. While sediment phosphorus concentrations are marginally elevated downstream, suggesting potential accumulation in the soil, further sampling is needed to confirm this trend. This study provides valuable insights into nutrient dynamics within agricultural drainage ditches and contributes to a better understanding of the effectiveness of sediment ponds as a potential mitigation measure for nutrient retention.
Fennoscandian AND Yamalian tree-ring anatomy shows a warmer modern than medieval climate
Authors: Björklund Jesper , Seftigen Kristina , Stoffel Markus , Fonti Marina V , Frank David C , Kottlow Sven , Esper Jan , Fonti Patrick , Goosse Hugues , Grudd Håkan , Gunnarson Björn E , Hantemirov Rashit , Klesse Stefan , Kukarskih Vladimir , Nievergelt Daniel , Pellizzari Elena , Carrer Marco , von Arx Georg
Published: March 2024

Earth system models and various climate proxy sources indicate that global warming is unprecedented during at least the Common Era. However, tree-ring proxies often estimate temperatures during the Medieval Climate Anomaly (950–1250 CE) to be similar, or exceed, those recorded for the past century. This is in contrast to simulation experiments at regional scales. This not only calls into question the reliability of models as well as proxies, but also contributes to uncertainty in future climate projections. Here we show that the current climate of Fennoscandia is substantially warmer than during the medieval period. This indicates a dominant role of anthropogenic forcing in climate warming even at the regional scale, thereby reconciling differences between reconstructions and model simulations. These results were obtained using an annually resolved 1,170-year-long tree-ring record that relies exclusively on tracheid anatomical measurements from Pinus sylvestris trees. Now we can confirm these results using new tree-ring anatomy data developed from Larix Sibirica tree-ring samples from the Yamal Peninsula in North-western Siberia over the past millennia. Both these datasets provide exceptional high-fidelity measurements of instrumental temperature variability during the warm season. We call for the construction of more such millennia-long records to continue to improve our understanding and reduce uncertainties around historical and future climate change at increasingly larger scales.
Data assimilation approaches with iterative ensemble smoothers in coupled nonlinear multiscale models
Authors: Vossepoel Femke , Evensen Geir , van Leeuwen Peter Jan
Published: March 2024

Iterative ensemble smoothers, originally developed for parameter estimation in petroleum applications, are effective data assimilation methods in coupled, unstable dynamical systems. In this study, we demonstrate this using a coupled multiscale model based on two Kuramoto-Sivashinsky equations with different spatial and temporal scales, representing two subsystems of an earth-system model. The cross-covariance between the variables of the two subsystems reflects how each subsystem influences the other, leading to unexpected structures that reveal interesting physics of the coupled system. The setup of the data assimilation allows simultaneous updating of both systems, leading to consistent estimates.A comparative study illustrates the properties of iterative ensemble smoothers and assimilation updates over finite-length assimilation windows. We demonstrate the increased accuracy of the smoothers’ solution compared to that of the standard ensemble Kalman filter and the fast convergence of the iterations related to the efficient handling of nonlinearities by the nonlinear space-time ensemble.Localisation, whether distance-based or adapted to spatial correlations, can be used in iterative ensemble smoothers to effectively deal with limited ensemble sizes. We discuss the effects of the spatial distribution and temporal frequency of available observations and illustrate how data gaps in one of the two subsystems affect the coupled estimate. Looking forward, we present the possibilities and benefits of a potential implementation of this approach in coupled earth-system models.
Healing of fault surfaces: a field vs. experimental perspective
Authors: Tesei Telemaco , Molli Giancarlo , Mittempergher Silvia , Pozzi Giacomo , Remitti Francesca
Published: March 2024

“Fault healing” is the ability of fault rocks to recover strength after rupture, due to a combination of several physical processes that include cementation, compaction, asperity growth etc. Healing is fundamental in the earthquake physics because it allows for the repeated accumulation of energy along faults over multiple seismic cycles. Fault healing is commonly studied in the laboratory, through Slide-Hold-Slide (SHS) tests and cementation experiments. However, laboratory measurements and the microstructures of experimental fault rocks are difficult to compare with natural rocks, due to the difference in kinetics of physical mechanisms and the small spatio-temporal scale of experiments.Here, we review the field and microstructural evidence of various processes of fault healing along a carbonatic fault surface, taking advantage of an outstanding case study: the Pietrasanta Normal Fault (NW Tuscany, Italy). In the field, the most common evidence of fault healing is the occurrence of cohesive fault rocks (cataclasites) and veins, but other fault surface properties may influence the re-strengthening of fault surfaces: e.g. adhesion phenomena (sidewall ripouts and fault surface patches) and geometrical complexity.We compare these observations with frictional healing experiments carried out on carbonatic fault rocks, in which both fault gouges and cohesive slip surfaces were used. We propose that a fault surface composed by “patches” of cohesive fault rocks bounded by anastomosing slip zones are the result of complex cycles of gouge formation and healing, which modulate the interplay of adhesion and localization along the fault surface.
Regional-scale monitoring of hillslope deformation through optical satellite imagery
Authors: Van Wyk de Vries Maximillian , Arrell Katherine , Basyal Gopi , Dadson Simon , Densmore Alexander , Di Martire Diego , Dunant Alexandre , Francioni Mirko , Guerriero Luigi , Harvey Erin , Jimee Ganesh , Kincey Mark , Li Sihan , Novellino Alessandro , Pujara Dammar , Shrestha Ram , Rosser Nick
Published: March 2024

Landslides are one of the most damaging disasters and have killed tens of thousands of people over the 21st century. Slow-moving landslides (i.e., those with surface velocities on the order of 10-2-101 m a-1) can be highly disruptive but are often overlooked in hazard inventories due to their subtle surface signatures and slow movement. Here, we discuss an approach to automatically map slow-moving landslides using feature tracking of freely- and globally-available Sentinel-2 optical satellite imagery.We evaluate this method through case studies from different environments in the USA, Chile, Italy, and Nepal. Our workflow identifies both known landslides and previously unknown slow-moving landslides in these case studies across very different geographical environments. In particular, in a test case on the well-documented Slumgullion earthflow, our workflow successfully delineates the active portion of the earthflow with velocity magnitudes consistent with field measurements. In another test case on the margin of the Southern Patagonian Icefield, Chile, we identified a very large (>6 km2) composite landslide in the eastern lateral moraine of Glacier Occidental, part of which catastrophically collapsed onto the glacier in early 2023. Finally, we tested our tool to the Ponzano landslide in central Italy which failed catastrophically in 2017.We are able to detect slow-moving landslides in complex environments using 10-m resolution globally available satellite imagery, all without any manual intervention. Taken together, this means that our workflow can be applied to any region on Earth, regardless of the availability of prior information. We leverage this workflow to conduct a preliminary national-scale survey of slow-moving landslides in Nepal, identifying over 10,000 deforming hillslopes across the country, many of which are populated. Improved mapping of the spatial distribution and surface displacement rates of slow-moving landslides will improve our understanding of their role in the multi-hazard chain and can direct detailed investigations into their dynamics.Figure: Large slow-moving landslide complex in the lateral moraine of Glaciar Oriental, Chilean Patagonia detected using our workflow.
Regional effects of paleoclimate history on the subsurface temperature distribution in Germany
Authors: Salis Gross Eskil , Frick Maximilian , Norden Ben , Mutz Sebastian G. , Fuchs Sven
Published: March 2024

Knowledge of the underground temperature distribution is crucial for evaluating geothermal potential and ensuring the long-term safety of heat-producing waste in repositories. Previous research, mainly conducted in Northern Europe and Canada, has shown that the Pleistocene Glaciations have an additive effect, resulting in a cooling of several degrees Celsius at depths of up to two kilometers. Recent studies indicate that the Last Glacial Period and the recent warming of the past 100–150 years have the greatest paleoclimatic impact on the current shallow to medium depth subsurface temperature distribution in Germany. If thermophysical properties of the subsurface are known, the distribution of underground temperatures can also be used to reconstruct the local ground surface temperature history using borehole climatology. Ground surface temperature reconstructions have low temporal resolutions, but they are directly reconstructed from temperature measurements without the use of climate proxies. Observations of the subsurface temperature distribution are limited to boreholes that are undisturbed by drilling or operations like production tests. Furthermore, the coupling of ground surface temperatures and surface air temperatures presents a significant challenge due to complex and transient surface processes associated with soil types, precipitation, vegetation, and the distribution of water bodies and glaciers. A systematic study of the paleoclimatic impact on the subsurface temperature distribution in sedimentary regions in Germany has not yet been conducted. Moreover, borehole climatology studies in Canada and Northern Europe has mainly concentrated on local reconstructions of ground surface temperatures, focusing on single or a limited number of boreholes. The aim of this study is to investigate the paleoclimatic effect of the Holocene on the subsurface temperature distribution in Germany and to quantify regional variations in the ground surface temperature histories. To achieve this, we have identified wells in sedimentary regions across the country that satisfy the prerequisites for borehole climatology. By using geophysical well logs, we derive the thermophysical characterization of the subsurface. We are examining the continuous temperature profiles to determine the magnitude, and regional variability of the Holocene paleoclimatic signal in borehole temperature profiles throughout Germany.
New surface exposure age data using cosmogenic radionuclides 10Be and 14C to constrain the age of the last deglaciation in the Retezat Mts, Southern Carpathians, Romania
Authors: Ruszkiczay-Rüdiger Zsófia , Kern Zoltán , Madarász Balázs , Urdea Petru , Braucher Régis , Molnár Mihály , Búró Botond , Team Aster
Published: March 2024

The presence of cosmogenic radionuclide concentrations inherited from previous exposure(s) of glacially transported boulders and moulded bedrock surfaces may hinder the determination of the surface exposure age (SED) of the last phase of (de)glaciation.A previous study revealed that glacial landforms of the cirque area in the southern side of the Retezat Mountains (Southern Carpathians, Romania) hold significant amount inherited 10Be (t1/2=1.4 My), which was used for a tentative estimation of the amount of glacial erosion, assuming that the lowest 10Be concentration was representative of the true age of deglaciation (Ruszkiczay-Rüdiger et al., 2021, Geomorphology 384, 107719).In this study, a western valley, the Zlătuia-Dobrunu valley of the Retezat Mts was sampled for 10Be SED. The novel data are in agreement with the previous datasets suggesting that the most extended glaciers belonged to the Last Glacial Maximum. However, the old apparent exposure durations based on 10Be analysis of samples from the cirque area provided firm evidence for the presence of excessive abundances of cosmogenic 10Be in this valley as well.The use of the short-lived in situ produced 14C (t1/2= 5.7 ky) provides an independent age constraint for the timing of the last deglaciation, because all 14C inventories that might be inherited from a previous exposure would have already been decayed. As a consequence, the 14C concentrations are not biased by inheritance, thus i) enable the age determination of the landforms belonging to the last phases of deglaciation and ii) the 14C exposure ages compared to the 10Be data will allow an assessment of the inherited amount of 10Be and thus a more precise determination of the amount of glacial erosion.In this study the new 10Be and 14C SED ages will be presented together with the mapped glacial landforms, reconstructed paleoglaciers and their Equilibrium Line Altitudes.Funding: NKFIH FK124807, INSU/CNRS, ANR - “EQUIPEX Investissement d’Avenir”, IRD and CEA, the PNRR-III-C9-2022 - I8, no. 760055/23.05.2023, CF 253/29.11.2022. and Horizon 2020 grant 871149 ”EUROPLANET”.
Simulated Sea Surface Salinity Data from a 1/48° Ocean Model 
Authors: Bingham Frederick , Fournier Séverine , Brodnitz Susannah , Hayashi Akiko , Kuusela Mikael , Westbrook Elizabeth , Carlin Karly , González-Haro Cristina , González-Gambau Verónica
Published: March 2024

In order to study the validation process for sea surface salinity (SSS) we have generated a year (November 2011- October 2012) of simulated satellite and in situ “ground truth” data. This was done using the ECCO (Estimating the Circulation and Climate of the Oceans) 1/48° simulation, the highest resolution ocean model currently available. The ground tracks of three satellites, Aquarius, SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) were extracted and used to sample the model with a gaussian weighting similar to that of the satellites. This produced simulated level 2 (L2) data. Simulated level 3 (L3) data were then produced by averaging L2 data onto a regular grid. The model was sampled to produce simulated Argo and tropical mooring SSS datasets. The Argo data were combined into a simulated gridded monthly 1° Argo product. The simulated data produced from this effort have been used to study sampling errors, matchups, subfootprint variability and the validation process for SSS at L2 and L3.
Groundwater recharge evaluation in a glacial buried valley using time series analyses of water levels.
Authors: Mendoza Alfredo
Published: March 2024

Groundwater hydraulics and aquifer processes are largely investigated using techniques that involve some kind of borehole test pumping. Such techniques are well established in the engineering and scientific community but are resource demanding. Time series analysis offer a collection of alternative methods that can be used to evaluate groundwater processes like recharge by using natural groundwater levels as input data. In this work, time series analyses were used to increase the hydrogeological knowledge of an area characterised by a glacial buried valley in southern Sweden. The hydrogeological environment is characterized by an upper aquifer (often clay till) and a deep aquifer consisting of different types of sediment (fine sand and gravel). The two aquifers are often separated by dense clays or massive low-permeability tills. The work aimed to implement time series analysis for estimating the amount of groundwater infiltrating at several points of the buried valley, which is one of the most important groundwater reservoirs in southern Sweden. The goals were to (a) contribute new knowledge about groundwater recharge and (b) evaluate the use of time series analysis to answer hydrogeological questions in this particular geological setting. The data processing steps included barometric compensation, Fast Fourier Transform (FFT) for identification of dominant unwanted frequencies, median filter to remove any disturbances in the signal, and corrections for possible signal shifts. Data filtering or correction was in some cases also applied to precipitation data and other meteorological parameters used to evaluate the reliability of the results. Recharge calculations were carried out using the water table variation method. The results suggest that for the period 2020 – 2021 the recharge was in the order of 350 mm for the upper aquifer and that around 10% of the precipitated water may be available for further infiltration to the deeper high-permeable sediments. Attempts to compare the obtained results with calculations made with the water budget method brought uncertainties as calculating the parameters for the water budget method implied using data with variable quality, scale and accuracy. However, using time series analysis has the potential to qualitatively monitor the recharge process in the area. The reliability of such recharge estimations can be supported by evapotranspiration measurements.
Impact of Canadian Wildfires 2023 on North Atlantic’s Region Air Quality: An Analysis Using ASDC Data
Authors: Mahmoud Hazem , Garcia-Solera Ingrid , Kaufman Daniel , Radkevich Alexander , Baskin Walter
Published: March 2024

The escalating threat of wildfires in North America raises significant concerns regarding their adverse effects on air quality and public health, as recent wildfires have resulted in widespread smoke plumes that transcend international borders. This study focuses on the exposure of the North Atlantic region to smoke from Canadian wildfires, underscoring the profound implications for public health and environmental well-being. To assess the air quality impact, we analyze satellite data obtained from the NASA Atmospheric Science Data Center (ASDC) at Langley Research Center, in conjunction with ground-based measurements and atmospheric modeling outputs. Specifically, we investigate  concentrations of atmospheric aerosols, notably PM2.5 particulate matter originating from Canadian wildfires, dispersion patterns, and the duration and intensity of smoke events affecting the North Atlantic. Utilizing data from multiple instruments — including those from the Earth Polychromatic Imaging Camera (EPIC), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP Lidar), and Measurement of Pollution in the Troposphere (MOPITT) — strengthens the conclusions drawn from the impact assessment and estimation of aerosol loading. Ground-based measurements, including data from air quality monitoring stations, provide localized information for validation and calibration purposes.The study's findings enhance understanding of the repercussions of Canadian wildfires on air quality in the North Atlantic region, underscoring the necessity of monitoring and prediction of transboundary smoke events through the integration of data from diverse sources, such as those provided by the ASDC. This information is pivotal for policymakers, public health officials, and residents in affected areas to formulate effective strategies in mitigating health risks associated with wildfire smoke and improving air quality during wildfire seasons. The study emphasizes the critical role of atmospheric remote sensing, particularly the use of ASDC data, in addressing the challenges posed by wildfires and their consequences on regional scales.
Hybrid AI permafrost modelling
Authors: Martinez Barberi Diego , Beltrami Hugo , Gondra Iker , Richards Agnes , Ouellet Felix , González Rouco Fidel , García-Bustamante Elena
Published: March 2024

Deep learning is an approach capable of extracting spatio-temporal features automatically while processing large amounts of data through complex structures. Structures that, for example, are able to learn from past patterns and share with the future if strong correlation is found. It could be assumed AI models only need to be built and gather enough data to find links between input and outputs. However, this approach cannot ensure that predictions would respect the laws of physics, e.g. due to extrapolation or observational biases. Restricting models by introducing physics can add strong theoretical guidelines alongside observations.In the context of permafrost models, data observations are lacking (e.g. wind speed or humidity) and models lose the possibility of spatial extrapolation. In this case, simplification of the physics is an usual procedure. Such that the problem is solved by an approximate solution that still captures broad spatio-temporal features while responding to more accessible predictors (e.g. surface air temperature or air pressure). More specifically, permafrost present-day thermal state is the consequence of past climate conditions that induced long-term variations of deep reservoirs of organic carbon and ground ice. Reproducing permafrost evolution at century to millennia scale requires models to operate with limited and highly uncertain information about thermal and hydrological ground properties.In need of both data and physical constraints, climate models themselves could be used as data generators. Here, CryoGrid Lite, a simplified version of the permafrost model CryoGrid 3, is used to simulate ground thermal regime and ice balance. Daily data of air temperature, pressure and geothermal flux run CryoGrid Lite to simulate the evolution of the thermal state of permafrost and active layer thickness over many centuries for the Canadian Arctic permafrost region. This dataset, generated by CryoGrid Lite, trains a neural network model to emulate its behavior. Physics equations governing the original model are also introduced into the objective function to penalize the network training when outputs exceed a tolerance range. This approach restricts outputs to the knowledge provided by CryoGrid Lite, enhancing physical reliability of forecasts. This is in contrast to the traditional 'black-box' structure of neural networks, which usually rely on minimizing errors with respect to observations. An assessment of the impact of including such additional constraints is provided. This study explores an hybrid approach between coupling physical process models with the flexibility of data-driven machine learning. The inclusion of physics within AI structures could improve their performance in permafrost modeling, while overcoming the reliability challenge that hampers its adoption in geoscience.
Improved treatment of snow over ice sheets in the NASA GISS climate model: towards ice sheet–climate coupling
Authors: Ringeisen Damien , Alexander Patrick , Roach Lettie , Mankoff Ken , Aleinov Igor
Published: March 2024

Representing the interactions between ice sheets and climate is essential for more accurate prediction of climate change and sea level rise. Ice sheets interact with the overlying atmosphere via the accumulation of snow and its compaction into firn, then ice, as well as the melting of surface snow and ice and the creation of runoff water. Getting an adequate representation of heat transfer, compaction, and melting processes is essential for an accurate representation of snow on land ice in global climate models. We are implementing an improved snow model on top of land ice as part of an effort to couple the NASA GISS climate model with the PISM ice sheet model. The new snow model includes additional layers and processes that are not currently incorporated (e.g., liquid water retention, percolation and refreezing, and snow densification), and mass and energy transfer methods that are consistent with both static ice sheets (with implicit iceberg fluxes) and interactive ice sheets (with explicit dynamics). We are tuning the densification scheme of this snow model with temperature and density data from common FirnCover and SumUp observations at locations in the accumulation zone of Greenland, and we compare the resulting density profiles to other SumUp density profiles in Greenland and Antarctica. We will assess the impact of this new snow model in climate model simulations with a static ice sheet compared with the previous (simpler) 2-layer snow model. Finally, we aim to use the non-coupled simulations as a baseline to assess the impact of dynamic coupling with an interactive ice sheet model.
In-situ structural analysis of silicate supercooled liquids through deep-UV Raman spectroscopy 
Authors: Cassetta Michele , D'Amico Francesco , Rossi Barbara , De Bona Emanuele , Sambugaro Alessia , Biesuz Mattia , Almeev Renat , Vetere Francesco , Giordano Daniele , Enrichi Francesco , Daldosso Nicola , Mariotto Gino
Published: March 2024

The understanding of the vitrification processes encompasses all the fields between geo- and material- sciences. Glass represents a non-equilibrium picture of its parental super-cooled liquid (SCL) and the last melt fraction quenched after a volcanic eruption. The SCL is usually read as a system that moves away from equilibrium without having enough time to explore the phase-space. In these conditions it is unable to find new configurations, causing a drop of the thermodynamic equilibrium and a glass is formed. This system is named non-ergodic and at eruptive temperatures represent a key component that may experiences fragmentation [1]. This particular state is generally referred to the thermal interval preceding the glass transition temperature (Tg, T at which the viscosity is 1012 Pa s) and the monitoring of its micro-structural evolution requires the less invasive experimental technique. With this regard we have used the deep UV-Raman spectroscopy to investigate a set of silicate model-glasses, loaded with different iron contents. This spectroscopic approach allows for the acquisition of fluorescence-free spectra with a maximized signal in the high-wavenumber region (between 850-1300 cm-1) thus providing the finest conditions for the assessment of the tetrahedral arrangement through the Qn deconvolution analysis (n represents the number of bridging oxygens) [2]. Here we correlate the trend of each Qn unit with temperature across the Tg-interval. Our results display a clear T-dependent rearrangement of the Qn distribution in function of iron content. Our finding, supported by differential scanning calorimetry, thermal dilatometry, Mössbauer, FTIR and ATR measurements, may deliver helpful insights into the thermal-dependent microstructural evolution through the Tg-interval and the viscous behavior of silicate SCLs.[1] D. B. Dingwell, Science 273, 1054 (1996).
[2] M. Cassetta, B. Rossi, S. Mazzocato, F. Vetere, G. Iezzi, A. Pisello, M. Zanatta, N. Daldosso, M. Giarola, and G. Mariotto, Chem. Geol. 644, 121867 (2024).