bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-term nitrogen deposition does not affect nitrous oxide, methane and carbon dioxide exchange of mature beech tree stems
Authors: Machacova Katerina , Schindler Thomas , Warlo Hannes , Guerrieri Rossella
Published: March 2024

European beech (Fagus sylvatica L.) is a native and widely grown tree species typical for upland forests of Central and Southeast Europe. The soils of beech forests are regarded as predominant sources of nitrous oxide (N2O), sinks of methane (CH4) and sources of carbon dioxide (CO2), while the contribution of beech trees themselves to the ecosystem greenhouse gas (GHG) exchange varies by gas species and site. Moreover, forest nitrogen (N) and carbon cycling, and thus N2O, CH4 and CO2 turnover processes are affected by N deposition, yet, the long-term effect of N deposition on GHG exchange of soil and mature trees is far from being understood.We aimed to investigate whether an increase in N deposition can alter forest GHG emissions. In September 2023, we measured N2O, CH4 and CO2 exchange of beech stems and adjacent soil, and various environmental parameters in a mature pre-alpine beech forest in Northeastern Italy, where a N manipulation experiment (4 treatments each replicated in 3 plots) has been carried out since 2015. Four experimental plots were selected: control (N0, only ambient deposition), canopy N addition (N30A, +30 kg ha-1 yr-1 sprayed over tree canopies) and soil N additions with two different doses (N30 and N60, +30 and +60 kg ha-1 yr-1, respectively).The stems of mature beech trees were net sinks of CH4 (-11.9 ± 3.6 mg ha-1 ground area h-1, median ± 95% confidence interval) and sources of CO2 (639 ± 137 g ha-1 h-1), their N2O exchange potential (3.36 ± 3.82 mg ha-1 h-1) was rather low. The stem fluxes of all three GHGs were not affected by nine years of N treatment.The long-term N deposition did not alter the soil CO2 emission (3015 ± 193 g ha-1 h-1). However, the N addition to the soil tended to increase the soil CH4 uptake (-642 ± 61 versus -901 ± 69 mg ha-1 h-1, N0+N30A versus N30+N60 plots). The soil N2O emissions were highest at the control plot (82.4 ± 33.9 mg ha-1 h-1), whereas the plots N30A and N60 showed significantly lower fluxes (1.56 ± 12.41 mg ha-1 h-1).Our preliminary results detected high spatial variability in stem and soil GHG fluxes, which might be rather connected to the variable site topography than to the long-term N deposition effect. Future detailed soil GHG flux measurements across all experimental plots replicates will help to understand this variability and the effect of N deposition on the GHG fluxes. AcknowledgementThis research was supported by the Ministry of Education, Youth and Sports of CR within the programs CzeCOS (grant number LM2023048) and LU - INTER-EXCELLENCE II (grant number LUC23162). We thank Federico Magnani and Alessandra Teglia from the University of Bologna and Reparto Carabinieri Biodiversità in Pian del Cansiglio for scientific and logistic support, respectively.
Contrasting future changes in the North Atlantic and Nordic Seas overturning circulations
Authors: Årthun Marius , Asbjørnsen Helene , Chafik Leon , Johnson Helen L. , Våge Kjetil
Published: March 2024

The Atlantic meridional overturning circulation (AMOC) carries warm and saline waters northwards near the surface and cold, dense waters southwards at depth. The northward branch of the AMOC terminates north of the Greenland-Scotland Ridge that separates the North Atlantic Ocean from the Nordic Seas and Arctic Ocean. Here, we use large ensemble simulations and CMIP6 models to show that future circulation changes in the subtropical North Atlantic (26.5°N) and in the Nordic Seas show contrasting behavior.In a high emission scenario (SSP585), CMIP6 models show a gradual weakening of the subtropical AMOC. This weakening can be deconstructed by quantifying changes in the Gulf Stream, Deep Western Boundary Current (DWBC), and gyre recirculation (Asbjørnsen & Årthun 2023). By the end of the century, the Gulf Stream weakens by 29% and the DWBC weakens by 47%. The gyre recirculation component shows a weakening of 12%, indicative of a weakened subtropical gyre. 33% of the Gulf Stream weakening is due to changes in winds.In contrast to the North Atlantic, the overturning circulation in the Nordic Seas increases throughout most of the 21st century as a result of changes in water mass transformation and horizontal circulation (Årthun et al. 2023). The increased Nordic Seas overturning is furthermore manifested in the overturning circulation in the eastern subpolar North Atlantic (OSNAP-East). A strengthened Nordic Seas overturning circulation could therefore be a stabilizing factor in the future AMOC. Årthun, M., Asbjørnsen, H., Chafik, L., Johnson, H. L., Våge, K. Future strengthening of the Nordic Seas overturning circulation. Nature Communications, 14, 2065 (2023). https://doi.org/10.1038/s41467-023-37846-6Asbjørnsen, H., & Årthun, M. (2023). Deconstructing future AMOC decline at 26.5°N. Geophysical Research Letters, 50, e2023GL103515. https://doi.org/10.1029/2023GL103515
Spring phenology of Swiss grasslands under climate change – variability and trends since 19
Authors: Calanca Pierluigi , Mariotte Pierre , Probo Massimiliano
Published: March 2024

Phenological observations provides important information for the agricultural management of grasslands, especially in spring, when a clear trade-off between increasing biomass and decreasing forage quality emerges as plant development progresses. For this reason, and in view of the impacts of climate change on agroecosystems and the needs for adapting farming activities to altered climatic conditions, there is much interest for understanding the long-term trends of key phenological stages.In this contribution we first provide an overview of 30 years of phenological observations carried out since 1995 at more than 50 sites located in Western Switzerland and covering an altitudinal belt ranging between 400 and 1400 m above sea level. Using the so-called Dactylis glomerata (cocksfoot) equivalent stage to assess the average plant development in these grasslands, we show that full heading has been advancing by about 2 days per decade over the 30 years of observations.Secondly, we demonstrate that observed dates of full heading and associated trends are very well matched by estimates obtained with a simple phenological model. We use this model, along with models for determining the end of the snow season and the beginning of the vegetation period, to extend the assessment of climate change impacts on grassland spring phenology in Western Switzerland back to 1961. We report on shifts and associated trends calculated along the altitudinal transect covered by our network.Finally, we compare the development of grassland phenology with the one of spring phases of other plant species and discuss our results in the context of the overall temperature evolution in Switzerland. We also emphasize a possible association of the variability in the spring phenology of grasslands with the long-term evolution of large-scale atmospheric circulation modes, and the implications of projected future changes in climatic conditions for the agricultural utilization of permanent grasslands.
A novel two-stage multi-step dynamic error correction model for improving streamflow forecast accuracy
Authors: Roy Abhinanda , Kasiviswanathan Kasiapillai S , Patidar Sandhya
Published: March 2024

The occurrences of floods in the recent past have significantly increased due to climate change and anthropogenic activities. Hence, reliable streamflow forecasts are crucial for minimizing the detrimental effects of flooding. However, forecast accuracy deteriorates besides elevated uncertainty when the lead time increases. Therefore, streamflow forecast should have improved accuracy with simultaneous uncertainty quantification to increase the model confidence for effective decision-making. The study proposes a novel two-stage multi-step dynamic error correction model to forecast up to 7 days ahead of streamflow, with the objective of no significant deterioration in accuracy. The framework is developed by integrating the process-based hydrological HBV model with the Bayesian-based Particle filter (PF) and machine learning-based Random Forest algorithm (RF). This facilitates combining the advantages of each model, i.e., process understanding ability of the HBV model, robust uncertainty quantifying ability of the PF technique, and relatively superior predictive ability of the RF algorithm. The model performance is quantified through several statistical performance error measures and uncertainty indices, with graphical performance indicators. The framework tested on the Beas and Sunkoshi river basins of India and Nepal exemplified the NSE of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation respectively for the 7-day ahead streamflow forecast. Hence, the proposed dynamic modeling framework can be considered as a potential tool to forecast streamflow without significant deterioration in the model accuracy even at increased lead times.  
The Multi-Channel Maximum-Likelihood (MCML) method: towards a cost-effective multisource estimation algorithm
Authors: Poste Benjamin , Abed-Meraim Karim , Charbit Maurice , Le Pichon Alexis , Listowski Constantino , Roueff François , Vergoz Julien
Published: March 2024

We present an improvement of the Multi-Channel Maximum-Likelihood (MCML) method [1]. This approach is based on the likelihood function derived from a multi-sensor stochastic model expressed in different frequency channels. Using the likelihood function, we determine, for the detection problem, the Generalized Likelihood Ratio (GLR) with a p-value threshold to discriminate signal of interest and noise. For the estimation of the slowness vector, we determine the Maximum Likelihood Estimation (MLE). Comparisons with synthetic and real datasets show that MCML, when implemented in the time-frequency domain, outperforms state-of-the-art detection algorithms in terms of detection probability and false alarm rate in poor signal-to-noise ratio scenarios. Mathematical extension of MCML is implemented in order to detect overlapping coherent signals in the same time frequency domain. This extension is based on the spectral matrix content. Taking into account the last detected source, the array response in the spectral matrix is iteratively subtracted to estimate a subsequent source of weaker energy. We show that the implemented approach allows detecting overlapping signals in the same time frequency window under various scenarios with varying signal-to-noise ratio (SNR), frequency bands and array geometry. Compared with the original method based on multiple maxima selection on the monosource likelihood function, the new approach yields improved wave parameter estimates. Results are illustrated by synthetics and real data recorded by stations part of the International Monitoring System (IMS). [1] B. Poste et al. (2022), The Multi-Channel Maximum-Likelihood (MCML) method: a new approach for infrasound detection and wave parameter estimation, Geophysical Journal International, https://doi.org/10.1093/gji/ggac377
Solar wind magnetic holes in the inner and outer heliosphere
Authors: Karlsson Tomas , Trollvik Henriette , Dimmock Andrew , Hadid Lina , Morooka Michiko , Volwerk Martin , Arró Giuseppe , Madanian Hadi , Callifano Francesco , Preisser Luis , Rojas Castillo Diana , Simon-Wedlund Cyril
Published: March 2024

Solar wind magnetic holes are small-scale, isolated decreases of the magnetic field strength. They are commonly divided into two types, linear and rotational magnetic holes, based on the rotation of the magnetic field vector from one side of the hole to the other. We present Solar Orbiter, Parker Solar Probem and MESSENGER measurements of magnetic holes from the inner heliosphere (~0.1-1.0 AU) and Cassini measurements from the outer heliosphere (~9 ̶ 10 AU). We compare properties such as rate of occurrence, and distributions of scale size, depth, and amount of magnetic field rotation, and discuss the findings in terms of local generation of magnetic holes, versus transport of magnetic holes generated in the inner heliosphere. We also discuss the relative importance of magnetic holes in interacting with the magnetospheres of planets in the inner and outer heliosphere.
Global emergence of regional heatwave hotspots outpaces climate model projections
Authors: Kornhuber Kai , Bartusek Samuel , Seager Richard , Ting Mingfang
Published: March 2024

Multiple recent record shattering weather events raise questions about the adequacy of climate models to effectively predict and prepare for unprecedented climate impacts on human life, infrastructure, and ecosystems. While it appears that  the record breaking global mean temperatures of 2023 are still within the models spread, we show that climate models fail to reproduce the observed emergence of several heatwave hotspots on impact relevant temporal and spatial scales. We identify several heatwave hotspots worldwide in which the warming of the tail of the distribution outpaces the mean warming. In these regions we find that the observed trends by far exceed what is projected by the multi-model mean of state-of-the-art model frameworks. In multiple regions, observed trends are outside of the model spread and persist irrespective of resolution or model design: We investigate models from HighResMip project (49 members) and CAM6 and the ECHAM5 SST forced large ensemble (60 members) and find that biases persist throughout the vast number of climate model experiments with different architectures (fully coupled and forced with observed SSTs), resolutions (25 km – 250km), and large numbers of realizations (Kornhuber et al. submitted).We discuss potential reasons for the models’ shortcomings. Recently we found that models tend to underestimate the surface anomalies of specific deep atmospheric circulation patterns, which were fundamental to some of the most extreme heat waves in the past (Kornhuber et al. Nat. Comms. 2023). Europe is identified as a heatwave hotspot, driven in part by trends in atmosphere dynamics (Rousi, Kornhuber et al. Nat. Comms. 2022), which models tend to understimate (Vautard et al. Nat. Comms 2023). In addition, non-linear interactions between high pressure, soil moisture deficiencies and temperature have been shown to be pivotal for the record shattering Pacific Northwest heatwave, amplifying its magnitude by 40% (Bartusek, Kornhuber, Ting, Nat. Climate Change 2022). Such dependence structures might not be accurately represented in most models. We conclude that while climate models have been useful and on point regarding their global mean temperature response to increased greenhouse gas concentrations, trends in the far-end tails of the temperature distribution are not well captured, missing emerging high-risk hotspots. This highlights the need to better understand and model the drivers of extreme heat and to rapidly mitigate greenhouse gas emissions to avoid further harm from climate surprises.  
Ocean heat transport modulates Arctic sea ice loss
Authors: Feltham Daniel , Aylmer Jake , Ferreira David
Published: March 2024

Decades of literature propose ocean heat transport (OHT) as a potential cause for uncertainty in the rates of Arctic sea ice loss based on strong correlations across different climate models. This study presents a simple underlying physical theory, derived from the large-scale energy budget of the polar region, that explains the impact of OHT on Arctic sea ice. Expressed in an intuitive linear equation, we show that this captures the relationship between historical and future Arctic sea ice loss, OHT, and polar warming, across 20 of the latest-generation climate models. Furthermore, using a lagged-correlation analysis, we find that changes in OHT lead the losses in sea ice, implying that OHT exerts a systematic modulation of historical and future Arctic sea ice loss. Our simple equation applies equally in the Southern hemisphere, for which Antarctic sea ice loss is also strongly correlated with the change in poleward OHT. However, here sea ice loss leads changes in the OHT, with further scrutiny of the other terms in our equation identifying changes to the atmospheric circulation as the primary driver. On the basis of these findings, we strongly advocate further research into the causes of different ocean (atmospheric) circulation changes in the Arctic (Antarctic) and better validation in climate models to ultimately reduce uncertainty in future projections. 
Dynamic Susceptibility of Rainfall-Induced Landslides: A Gated Recurrent Unit Approach
Authors: Lim Jana , Santinelli Giorgio , Dahal Ashok , Vrieling Anton , Lombardo Luigi
Published: March 2024

Globally, there is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on a single aggregated measure of rainfall derived from either in-situ measurements or radar estimates. Relying on a summary metric of precipitation may not capture the intricacies of rainfall dynamics that could improve landslide prediction. Here, we present a proof-of-concept for constructing a LEWS that is based on an integrated spatio-temporal modelling framework. Our proposed protocol builds upon a recent approach that uses the entirety of the rainfall time series instead of the traditional cumulated scalar approximation. Specifically, we use a Gated Recurrent Unit to process the whole rainfall signal and combine the output features with a second neural network dedicated to incorporating terrain characteristics. We benchmark this approach against a baseline run that relies on terrain and a cumulative rainfall metric. Our protocol leads to better performance in the context of hindcasting landslides which uses past rainfall estimates from CHIRPS. This provides a stronger case to repeat the same experiment using weather forecasts. If analogous results are produced in the forecasting context, this could justify adopting such models for operational purposes.  
Prediction of soil phosphorus sorption capacity in agricultural soils using mid-infrared spectroscopy. 
Authors: Yang Sifan , White Blánaid , Regan Fiona , Kent Nigel , Hall Rebecca , de Santana Felipe , Daly Karen
Published: March 2024

             Advice for phosphorus (P) fertilisation based on soil testing using extractive methods but does not consider P sorption processes. Traditional soil P sorption capacity examined from a Langmuir isotherm batch experimental design, which is time-consuming, labour intensive and expensive. Mid-infrared (MIR) spectroscopy is a rapid analysis technique that can potentially replace the extractive technique traditionally used in soil analysis. The objective of this work was to predict the isothermal parameter of P sorption maximum capacity (Smax, mg·kg-1) from MIR spectroscopy.              This study created spectral libraries from benchtop (Bruker) and handheld (Agilent) MIR spectrometers by scanning samples in two particle sizes, < 0.100 mm (ball-milled) and < 2 mm. The four spectral libraries created used an archive of samples with a database of sorption parameters where soils were classified into low and high sorption capacities using a threshold value of Smax = 450.03 mg·kg-1. To assess the optimal algorithmic method with highest Smax prediction accuracy, regression models were based on the partial least squares (PLS) regression, Cubist, support vector machine (SVM) regression and random forest (RF) regression algorithms. After the first derivative Savitzky-Golay smoothing, Bruker spectroscopies with both soil particle sizes yielded ‘excellent models’, with SVM predicting Smax values with high accuracy (RPIQVal = 4.50 and 4.25 for the spectral libraries of the ball-milled and
A universal method to isolate, enrich, and quantify atmospheric organosulfates
Authors: Ma Jialiang , Zhao Cunliang , Reininger Natalie , Vogel Alexander
Published: March 2024

Organosulfates (OSs) are ubiquitous compounds in ambient aerosols, being formed by multiphase chemistry.1,2 However, accurate quantification of unknown and suspected OSs remains difﬁcult. One of the main reasons for this is the limited availability of authentic standards. Furthermore, a large amount of unknown and not yet identified OSs might remain undetected in ambient samples.To overcome the two main difﬁculties, we developed a new solid phase extraction (SPE) method to enrich and fractionate OSs compounds. Preliminary result shows that with this approach the majority of the OSs fraction was separated from the native sample matrix. Furthermore, we can easily enrich the native extract by a factor of ~300 and get good recovery of those targeted OS-compounds. Following this SPE-method, a charged aerosol detector (CAD) was employed for the quantification of the OSs fraction extracts after chromatographic separation. CAD, which has universal response as a prominent feature, is advantageous to the quantification of non-volatile species without the necessity for the preparation of authentic or surrogate standards.3 The sample preparation by SPE greatly reduced the complexity of both chromatograms detected with a mass spectrometer and the CAD, thereby increasing our confidence for the peak identification and quantification. Although, CAD has a sub-nanogram sensitivity, our classical extraction method for ambient filter samples most OSs are below the limit of detection. Therefore, the SPE enrichment of an ambient filter extraction is necessary for CAD detection.Moreover, the volatility for majority OSs compounds is very low, which perfectly match the CAD feature. With this method we can identify and quantify ~40 OSs compounds and nitrooxy-OSs in an ambient Chinese PM2.5 sample. This work emphasizes the potential of the SPE approach in combination with CAD to quantify the unknown and suspected OSs precisely in ambient air. The presented method is able to quantify the individual OS compounds without authentic or surrogate standards. References1. Brüggemann, M. et al. Organosulfates in Ambient Aerosol: State of Knowledge and Future Research Directions on Formation, Abundance, Fate, and Importance. Environ. Sci. Technol. 54, 3767–3782 (2020).2. Gao, K. & Zhu, T. Analytical methods for organosulfate detection in aerosol particles: Current status and future perspectives. Sci. Total Environ. 784, 1–10 (2021).3. Vehovec, T. & Obreza, A. Review of operating principle and applications of the charged aerosol detector. J. Chromatogr. A 1217, 1549–1556 (2010).
Seasonal forecasting of the European North-West shelf seas: limits of winter and summer sea surface temperature predictability
Authors: Atkins Jamie , Tinker Jonathan , Graham Jennifer , Scaife Adam , Halloran Paul
Published: March 2024

The European North-West shelf seas (NWS) support economic interests and provide environmental services to several adjacent populous countries. Skilful seasonal forecasts of the NWS would be useful to support decision making. Here, we quantify the skill of an operational large-ensemble ocean-atmosphere coupled dynamical forecasting system (GloSea), as well as a benchmark persistence forecasting system, for predictions of NWS sea surface temperature (SST) at 2-4 months lead time in winter and summer. We also identify sources of- and limits to NWS SST predictability with a view to what additional skill may be available in the future. We find that GloSea NWS SST skill is generally high in winter and low in summer. Persistence of anomalies in the initial conditions contributes substantially to predictability. GloSea outperforms simple persistence forecasts, by adding atmospheric variability information, but only to a modest extent. Where persistence is low – for example in seasonally stratified regions – both GloSea and persistence forecasts show lower skill. GloSea skill can be degradeded by model deficiencies in the relatively coarse global ocean component, which lacks a tidal regime and likely fails to properly fine-scale NWS physics. However, using “near perfect atmosphere” tests, we show potential for improving predictability of currently low performing regions if atmospheric circulation forecasts can be improved, underlining the importance of development of atmosphere-ocean coupled models for NWS seasonal forecasting applications.
Converging Deep Learning and Numerical Prediction for Skillful Subseasonal Soil Moisture and Flash Drought Forecasting
Authors: Lesinger Kyle , Tian Di
Published: March 2024

Global warming is accelerating drought onset, causing more frequent flash drought events. These events occur at the subseasonal timescale in which rapid decreases in root-zone soil moisture (RZSM) increase risks of crop failure, wildfire, and heat stress globally. However, forecasting soil moisture and flash droughts at lead times beyond 2 weeks remains a significant challenge. Recently, machine learning methods with historical reanalysis data have shown improved forecast accuracy compared to state-of-the-art numerical weather prediction methods, but they can only produce skillful forecast within 10 days. Here we show that a convergence forecast model combining a deep learning approach with subseasonal retrospective forecasts (reforecast) from numerical models produces skillful subseasonal soil moisture and flash drought forecasts at lead times beyond 2 weeks. We train a deep learning architecture on combinations of reanalysis and reforecast from 2000 to 2015 and validate results during the testing period from 2018 to 2019. The subseasonal forecast skill of soil moisture of the convergence forecast model is much higher than those of current state-of-the-art numerical forecast models, deep learning bias corrected numerical forecast models, or the reanalysis-based deep learning models, which showed no skill after 2 weeks lead time. The convergence model also showed significantly improved performance for predicting flash droughts compared to the original or deep learning bias corrected numerical forecast models or reanalysis-based deep learning models.  A permutation analysis indicates that reanalysis precursors and soil moisture reforecast at lead times within 2 weeks both contribute significantly to the forecast skill at longer lead times. The convergence forecast model provides accurate and efficient subseasonal soil moisture and flash drought forecasting and is promising for accurately forecasting key variables and extreme events at the subseasonal timescale.
New vision on the Ionian Sea abyssal variability through integrated approaches.
Authors: Giambenedetti Beatrice , Lo Bue Nadia , Artale Vincenzo
Published: March 2024

The processes involved in abyssal variability, their connections to the water column above, and their vulnerability to climate change are still unknown. This is due to a dearth of long-term observations below 2 km of depth. To fill these gaps, our approach leverages existing datasets using different methodologies. The Ionian Sea in the eastern Mediterranean is a unique location to investigate abyssal dynamics considering that it serves as a gateway between two major deep-water formation zones i.e., the Adriatic and Aegean seas. Moreover, the observed severe thermohaline variations experienced by the bottom layers of the Ionian Sea during the last decades, had dramatic effects on the deep circulation, leaving their signature on the whole Mediterranean Sea. The variation of the deep stratification, the causes that generate it, and the impact it produces are still under debate. The mechanisms involved in such variability, as well as the connection with the abyss and the above layers of the water column, are not fully understood. Through tidal and fine-scale mixing analysis, seafloor observations, and a tailored semi-analytical model we depicted the critical role of stratification, morphology, and tides in enhancing local diffusivity in the abyssal layer. Stratification, in particular, influences stability and the rotation of vorticity among different density layers, linking deep-sea variability to the entire water column. We achieved these results by combining different indirect methodologies exploiting all available resources at best with a synergic approach, getting a possible representation of what happens at depths.
Thrust system growth by segment linkage in sedimentary multilayers
Authors: Butler Rob , Robledo Francisca , Sleath Phoebe , Bond Clare
Published: March 2024

This presentation challenges the conventional theory that thrust systems necessarily grow by the upward propagation of tip-lines of faults away from a basal detachment. The model underpins mechanical considerations of thrust belt dynamics, explanations of thrust-related folding and commensurate forecasting of distributed deformation in the surrounding strata. Yet there have been few tests of the paradigm at the cross-section scale – not least because the quality of seismic imagery in most foreland thrust belts is insufficient to resolve structural geometries. Rather, the widespread application of the upward propagation (conventional) theory results from a variety of cognitive biases, especially restrictive citation of published literature.Here we present interpretations of exceptional 3D seismic imagery from the eastern, lateral termination of the Jura fold-thrust belt of Switzerland. The area (the Baden-Irchel-Herdern Zone, referred to here as the BIHZ - broadly overlying the eponymous basement lineament) shows relatively little horizontal shortening and therefore preserves structures that would otherwise be overprinted by more extensive thrusting.  Contractional structures are developed in a multilayer of Mesozoic strata that comprise competent carbonates and incompetent mudrocks and marlstones. Interpretations based on 2D seismic profiles display thrusts sweeping through the Mesozoic strata as simple, continuous ramps, splaying from a basal detachment in Triassic evaporites. However, the higher-quality 3D seismic imagery does not support this type of interpretation. Within the BIHZ, statal reflectors are offset by small-offset (
Foundation Models for Science: Potential, Challenges, and the Path Forward
Authors: Maskey Manil , Ramachandran Rahul , Lee Tsengdar , Murphy Kevin , Roy Sujit , Ramasubramanian Muthukumaran , Gurung Iksha , Ganti Raghu
Published: March 2024

Foundation models signify a significant shift in AI by creating large-scale machine learning models (FMs) pre-trained on wide-ranging datasets. These models act as flexible starting points, ready to be fine-tuned for various specialized tasks. Distinct from traditional models designed for narrow objectives, foundation models apply their broad pre-training to learn patterns across data, enhancing their adaptability and efficiency in diverse domains. This approach minimizes the necessity for extensive, task-specific labeled datasets and prolonged training periods. A single foundation model can be tailored for many scientific applications, often outperforming traditional models in some tasks, even when labeled data is scarce. Addressing the right array of complex scientific challenges using AI FMs requires interdisciplinary teams from various groups and organizations. No single research group or institution can independently muster the necessary resources or expertise to construct useful AI FMs. Thus, collaborative efforts are essential, combining diverse skills, resources, and viewpoints to create more comprehensive solutions. The right blend of domain-specific expertise and a broad understanding of various AI subfields is crucial to ensure the versatility and adaptability of foundation models. Moreover, the scientific community must develop a wide array of use cases, labeled datasets, and benchmarks to evaluate these models effectively across different scenarios to be accepted and widely utilized within science. Building Foundation Models for science demands fostering collaboration among a diverse spectrum of research groups to ensure this broad range of perspectives. This strategy should include stakeholders like individual researchers, academic and government institutions, and tech companies. Embedding this collaboration within the principles of open science is therefore vital. Open science calls for transparent research, open sharing of findings, promoting reproducibility by making methodologies and data accessible, and providing tools researchers can freely use, modify, and distribute. Encouraging community collaboration in the model pre-training development leads to more robust and functional FM. Guaranteeing open access to datasets, models, and fine-tuning code enables researchers to validate findings and build upon previous work, thus reducing redundancy in data collection and cultivating a culture of shared knowledge and progress.
Experimental laboratory study of hydraulic fracture interaction with pre-existing fault
Authors: Turuntaev Sergey , Zenchenko Evgeny , Zenchenko Petr , Nachev Victor , Chumakov Tikhon
Published: March 2024

Hydraulic fracturing remains the primary method of increasing hydrocarbon inflow to a borehole. Despite the many years of experience in using this method and the existence of various hydraulic fracturing simulators, companies often face problems during hydraulic fracturing, which are associated with insufficient elaboration of the physical models used in these simulators. There are a lot of theoretical and experimental studies of the occurrence and propagation of hydraulic fractures. All the models have their limitations, which can be evaluated by conducting experiments on natural or artificial rock samples. An essential aspect of hydraulic fracturing is accounting for the natural fractures of rocks. On the one hand, the natural fractures can increase the hydraulic fracturing efficiency. On the other hand, the interaction of hydraulic fractures with tectonic faults can lead to undesirable consequences in the form of induced earthquakes.We present the results of laboratory experiments on the study of a hydraulic fracture interaction with a preliminary created fracture that simulates a natural fracture. In the experiments, under conditions of triaxial loading, the initiation and growth of a hydraulic fracture in a poroelastic material initially containing a fracture was investigated. A distinctive feature of the experiments is the ability to use an ultrasonic sounding to measure the fracture propagation and opening simultaneously with the fluid pore pressure measurements at the several points of the porous saturated sample. It allows to obtain the pressure distributions at various experiment stages and to establish a relation between the pore pressure distribution and the hydraulic fracture propagation and its interaction with pre-existing fracture. The possibilities of active ultrasonic monitoring have been expanded due to preliminary calibration experiments, which make it possible to measure the fracture opening value by attenuation of ultrasonic pulses. The profile of the existing fracture opening has been restored.The experiments show that the fracture propagation is influenced by the natural fracture. This is caused by the hydraulic fracturing fluid leaks into the natural fracture, so both hydraulic fracture and natural fracture compose united hydraulic system. The results obtained can be used to refine models of secondary fracture network formations during hydraulic fracturing in unconventional fractured reservoirs.
A data fusion uncertainty-enabled method to map street-scale hourly NO2: a case study in Barcelona
Authors: Criado Alvaro , Mateu Armengol Jan , Petetin Hervé , Rodríguez-Rey Daniel , Benavides Jaime , Carnerero Cristina , Guevara Marc , Pérez García-Pando Carlos , Soret Albert , Jorba Oriol
Published: March 2024

Considering that air pollution is the leading global environmental risk factor according to the WHO,  characterizing NO2 levels holds crucial significance, particularly in heavily trafficked urban areas where NO2 legal limits and health guidelines are frequently exceeded. Obtaining accurate and comprehensive NO2 datasets on a city level is especially challenging due to the inherent uncertainties associated with urban air quality models, and the scarcity of air quality monitoring stations. An alternative method to describe NO2 levels involves developing short-term experimental campaigns using indicative measurements, although they report period-averaged results and do not have full spatial coverage. Taking advantage of the three mentioned approaches,  this work proposes a data-fusion method that combines i) near-real-time hourly observations obtained from the official air quality monitoring network, ii) the output of an urban air quality model (CALIOPE-Urban) that operates at high spatial (up to 20m x 20m) and temporal (hourly) resolutions, and iii) a microscale Land-Use-Regression (LUR) model based on machine learning. The microscale-LUR model includes different urban datasets such as traffic flow or average building density and two NO2 experimental campaigns. While the hourly observations enable the temporal variability adjustment in the dispersion model, the microscale-LUR model provides additional insights into the spatial characteristics of NO2 distribution. Our data-fusion approach was implemented on an hourly basis over the metropolitan area of Barcelona in 2019. Besides the bias-corrected NO2 hourly maps, this method also computes the uncertainty associated with the variance of the estimated error during the correction process. By integrating both corrected NO2 values and their associated uncertainty, it produces maps that show the probability of exceeding the hourly 200 µg/m3 and the annual 40 µg/m3 NO2 legal thresholds over Barcelona. Cross-validated results at the monitoring stations demonstrate that the spatial bias correction increases the correlation coefficient (r) by +46 % and decreases the root mean square error (RMSE) by −48 %, compared to the model output. This research emphasizes the importance of highly detailed spatial data within data-fusion techniques, enhancing the accuracy of predicting exceedances at the street level.
A unique look into European middle Eocene weather; Comparing a new synoptic-scale proxy record to simulated daily conditions.
Authors: Baatsen Michiel , van Horebeek Nick , Ziegler Martin , de Winter Niels , Speijer Robert , Vellekoop Johan
Published: March 2024

In addition to improving our general understanding of the climate system, the study of past warm climates is often stated relevant due to the possible resemblances to various future scnenarios. The comparison between proxy records and climate model simulations offers opportunities for validation beyond the boundaries of present climatic conditions. Besides their scarcity in both spatial and temporal coverage, existing proxies pre-dating the Pleistocene only provide an integrated signal typically over 1000 years or more. Climate model simulations provide much more data beyond that which can be validated using proxies, that is therefore often not considered in palaeoclimate studies.A unique opportunity presents itself with a new ultra-high resolution record of the middle Eocene, obtained from the giant marine gastropod Campanile giganteum. Found in the Paris Basin (palaeolatitude 40-45N), these gastropods lived in a shallow marine environment and reached growth rates of over 600mm/year. Well-preserved fossils, in combination with such high growth rates, provide the first record to our knowledge resolving weather-timescale variability in the Eocene.In this study, we interpret this snapshot of European middle Eocene weather and compare our findings to daily model fields using the CESM 1.0.5. We continued existing 38Ma simulations using 4 times pre-industrial CO2 and CH4, which were shown previously to be a good match with the middle Eocene climate (see: Baatsen et al. 2020, Climate of the Past, doi: 10.5194/cp-16-2573-2020). The proxy record shows distinctly different seasonalities of temperature and salinity over central Europe in the middle Eocene. We can interpret these patterns as indications of a monsoonal climate with notably high variability in summer precipitation. Such a climatic regime is supported by the model, which also shows monsoonal conditions over central Europe as well as northern Africa. The agreement between the proxy record and the simulations, on both seasonal and synoptic scales, thus promotes further interpretation of these model results on an entirely new scale.
The intensity of interglacials during the last 800 kyr
Authors: Wolff Eric , Capron Emilie , Tzedakis Polychronis , Legrain Etienne , Mitsui Takahito , Yin Qiuzhen
Published: March 2024

An ultimate target of Quaternary climate studies is to predict the strength and timing of glacial cycles using only the Milankovic forcing as input.  Here we consider just one aspect of this challenge, the intensity of interglacials. Previous work (PIGS Working Group, 2015) has identified 11 interglacials in the last 800 kyr. Are some of them globally strong or weak? Is there a step change at the mid-Brunhes (between MIS 13 and MIS11)? And what controls the observed intensity?We first discuss what we mean by intensity.  Some datasets (such as mean global temperature or sea level) have a more global character and might be considered more robust indicators of interglacial strength, but are more difficult to estimate compared to simpler parameters such as CO2 concentration and Antarctic temperature.  Many records show “overshoots”, temporary maxima that are followed by longer plateaus of interglacial character.  Despite these complications, some patterns do emerge. In global scale records, MIS 5e, 11, 9, 1 stand out as particularly warm, with 13 and 17 particularly cold. Some terrestrial records show a different pattern with MIS 13 unusually strong in many Asian records.  There is a tendency to more intense interglacials after 450 ka, but MIS 7e and 7c would sit quite happily in the pre-mid-Brunhes pattern.A first look at the astronomical/orbital context is not encouraging. We see the obvious MIS11 paradox, that weak precessional forcing leads to a strong interglacial (or the opposite, most clearly seen in MIS 15e and 7c). However two different approaches have been quite successful, and may point the way to a more satisfying conclusion. Yin and Berger (2010, 2012) predicted the strength of interglacials using Milankovic forcing plus CO2 concentration as inputs.  This approach suggests that the main cause of stronger interglacials after the mid-Brunhes is higher CO2 and pushes the problem into understanding the controls on the intensity of CO2 maxima. Mitsui et al (2022) used Milankovic forcing plus the strength of the previous glacial. In this model, the tendency to stronger interglacials after the mid-Brunhes arises essentially from a tendency to higher obliquity, as part of a 1.2 Myr cycle. Neither approach views the change across the mid-Brunhes as an ”event” and we propose it should rather be termed a mid-Brunhes “Shift” (MBS).Here we discuss how we might approach a unified explanation that draws on both models, with periods of highest CO2 perhaps being related to the pattern and timing of AMOC strength during the termination. This is influenced by the size of glacial ice sheets and by orbital intensity through their influence on the amount of freshwater available and the rate at which it is delivered into the ocean.  Finally we consider whether the pattern of obliquity is enough to understand the MBS, i.e. is it part of a longer term oscillation.
A human driver of change in the southern Baltic coastal sedimentary system: monitoring effects of dredging spoil dumping on benthic communities 
Authors: Radziejewska Teresa , Wawrzyniak-Wydrowska Brygida , Bieniek Bartosz
Published: March 2024

Construction of coastal infrastructure, e.g. seaward port facilities, frequently calls for sediment removal (dredging). Deposition of the dredging spoil at designated offshore sites (dumping grounds) disturbs the dumping ground sedimentary system, including the biota. Assessment of environmental effects of dumping requires monitoring of the system’s responses to the disturbance severity and persistence. In 2011-2017, we followed changes in sediment characteristics and descriptors of benthic (meio- and macrofaunal) assemblages (abundance, biomass, composition) in a shallow southern Baltic coastal area serving as a dumping site for dredging waste from a new harbour under construction at the coast. At the initial phase of the disturbance, the benthos responded rapidly (abundance and biomass reduction, altered composition), and equally rapidly recovered when dumping was temporarily suspended. After the dumping operations were resumed, the responses intensified, although apparent colonizers (benthic copepods in the meiobenthos and juvenile molluscs in the macrobenthos) tended to appear intermittently in the disturbed areas. The benthos remained impoverished in the altered habitat after dumping was terminated, reflecting the severity of habitat change.
Peat soil thickness and carbon storage in the Belgian High Fens: insights from multi-sensor UAV remote sensing
Authors: Li Yanfei , Jonard François , Henrion Maud , Moore Angus , Lambot Sébastien , Opfergelt Sophie , Vanacker Veerle , Van Oost Kristof
Published: March 2024

Peatlands are known to store a large amount of carbon, but global warming and associated changes in hydrology have the potential to accelerate peatland carbon emissions. An in-depth understanding of carbon dynamics within these peatlands is therefore important. However, peatlands are complex ecosystems, and acquiring accurate and reliable estimates of how much carbon is stored underneath the Earth’s surface is inherently challenging even at small scales. Here, Unmanned Aerial Vehicles (UAVs) equipped with RGB, multispectral, thermal infrared, and LiDAR sensors were combined with Ground Penetrating Radar (GPR) technology and traditional field surveys, to provide a comprehensive 4D monitoring of a peatland landscape in the Belgian High Fens. Data was collected along a hillslope-floodplain transition. We aimed to establish links between the above- and below-ground factors that control soil carbon status, identify the key drivers of carbon storage as well as explore the potential of UAV remote sensing for spatial mapping of peat depth and carbon stock. Our results indicated that peat thickness widely varied (0.2 to 2.1 m) at small scales and is negatively correlated with elevation (r= -0.39, p