bluuebunny/crossref_metadata_2025 huggingface dataset:
Validation of GEMS tropospheric NO2 with the GEMS IUP-UB NO2 product, the TROPOMI NO2 product, and ground-based DOAS measurements
Authors: Lange Kezia , Richter Andreas , Bösch Tim , Zilker Bianca , Burrows John P. , Bösch Hartmut , Merlaud Alexis , Fayt Caroline , Friedrich Martina M. , Van Roozendale Michel , Ziegler Steffen , Ripperger-Lukosiunaite Simona , Wagner Thomas , Kim Donghee , Chang Lim-Seok , Hong Hyunkee , Bae Kangho , Song Chang-Keun , Lee Hanlim
Published: March 2024

Nitrogen dioxide (NO2) is one of the most important air pollutants in the troposphere. NO2 can be retrieved by differential optical absorption spectroscopy measurements, which can be performed from various platforms.Measurements from low earth satellites in sun-synchronous orbits provide a global overview and have already contributed valuable insights into understanding NO2. The latest instrument, TROPOMI, with its high spatial resolution of 3.5 x 5.5 km2, has given new opportunities to disentangle and analyze NOx sources. However, instruments in low-earth orbits usually provide only one measurement per day at each location.To achieve diurnal cycles of trace gases, instruments on geostationary satellites are needed. The Korean instrument GEMS on GK2B, launched in February 2020, is the first instrument in geostationary orbit, delivering hourly daytime observations of NO2 with a spatial resolution of 3.5 x 8 km2 over a large part of Asia.In this study, one year of tropospheric NO2 vertical column densities (VCDs) of the operational GEMS product are compared to the scientific GEMS IUP-UB NO2 VCD product, the operational TROPOMI NO2 VCD product, and ground-based DOAS measurements in Korea. The diurnal variation of NO2 observed by GEMS is compared to the diurnal variation observed at several ground-based MAX-DOAS stations located in different pollution regimes in Korea. The large variety of observed diurnal cycles are interpreted regarding potential influencing factors. In this respect, the ERA5 10 m wind data provide valuable insights into the influence of transport effects on the tropospheric NO2 VCD depending on station location and seasonality.
Organization of convection over Amazonia and its impact on transport
Authors: Dauhut Thibaut , Gonthier Héléna , Viala Bastien , Haytzmann Guido
Published: March 2024

Deep convection over Amazonia can manifest in various forms, from scattered convective cells to mesoscale organizations like squall lines and cloud clusters. This diversity significantly influences vertical convective transport, impacting not only large-scale circulation but also the poorly understood cycle of gases and aerosols emitted by the forest. Monitoring convective systems over Amazonia during the CAFE-Brazil field campaign (Dec 2022-Jan 2023) involved the HALO aircraft and the ATTO-Campina ground site, employing meteorological, aerosol, and chemical measurements.On January 18, a 500-km wide mesoscale system dissipated, giving rise to new convective cells initially disorganized and later organized into a large squall line. This event was measured by ATTO-Campina and HALO during the local afternoon. To understand the processes driving organizational changes and their impact on transport, 24-hour simulations with the Meso-NH model were conducted over an 800-km wide domain, ranging from horizontal resolutions of 1600 m down to 200 m, ultimately resulting in large-eddy simulations.The simulations revealed a strong resolution sensitivity in mesoscale convective organization, with a distinct emergence of squall lines at the finest resolutions only. Surprisingly, at fine resolution, organized convection exhibited larger transport due to increased updraft size, rather than intensity. Cloud cluster organization exhibited a delayed onset compared to convective cell organization, aligning with expectations. Ongoing investigations are currently focusing on gravity waves and cold pools to better understand their impact on convective organization.
Assessing N cycling and N2O emissions from ley within a crop rotation using measurements and modeling 
Authors: Wang Yuqiao , Ammann Christof
Published: March 2024

Nitrous oxide (N2O) is a significant greenhouse gas that contributes to climate change, with one of the major sources being agricultural fertilizer application. In Europe, grass-clover ley plays a crucial role in the agricultural ecosystem. However, N2O emissions and emission factors (EFs) associated with it are not well documented. We therefore monitored N2O emissions in grass-clover ley and assessed the feasibility of using the process-based model DayCent to simulate N2O emissions and the underlying N cycling. The experiment was undertaken in a long-term fertilization experiment on a ley–arable rotation in Switzerland. We compared N2O emissions and EFs from organic fertilizer (slurry), mineral fertilizer and control (unfertilized) plots over three years (2021–2023). The results showed average N2O emissions of 0.50 ± 0.08, 0.51 ± 0.03 and 0.01 ± 0.04 kg N ha-1 yr-1 from organic, mineral and control treatments, respectively. The N2O EF, which was determined from measured emissions of the fertilized treatments after subtracting of the control treatment, was much lower than the IPCC default of 1%, with values of 0.22% and 0.40% for organic and mineral fertilizer treatments, respectively. DayCent was adjusted for plant C/N ratio parameters and the biological N fixation parameter. It accurately predicted soil moisture, soil temperature, and aboveground N yield, with deviations of 2.2%, 4.4%, and 6.0% from the measured values. Concerning N2O emissions, DayCent simulated average EFs of 0.24% and 0.28% for organic and mineral fertilizers, respectively, suggesting a good agreement with the measurements. Our field observation and model simulation results indicate that using IPCC default EF may overestimate the N2O emission from grass-clover ley, and that, DayCent is able to reproduce the comparatively low N2O emissions. 
Lagrangian analysis of tracked anticyclonic structures in reanalysis data 
Authors: Thomas Michael , Pfahl Stephan
Published: March 2024

Since heatwaves are among the most impactful natural hazards, a better understanding of the atmospheric processes that drive near-surface temperature extremes may help to mitigate the future outcomes of such events in a warming climate. One common ingredient for mid-latitude summer heatwaves are blocking anticyclones, quasi-stationary and persistent high pressure areas, whose stable and mostly cloud-free conditions can favor the buildup of heat. A strong link between blocking anticyclones and heatwaves has been demonstrated in previous studies, but the physical processes leading to both the near surface temperature extremes and the blocking conditions are still debated. Consequently, the question arises as to what distinguishes blocking high-pressure systems from non-blocking ones and why some lead to near-surface temperature extremes while others do not.To address this question, a threshold-based tracking algorithm is applied to mid-tropospheric geopotential height anomalies within a 40 year period in ERA5 reanalysis data. The tracking itself is restricted to the extended summer period (MJJAS) and the Northern Hemisphere and the resulting spatio-temporal structures are labeled and classified based on their overlap with regions over Europe marked as (un)affected by blocking or heatwaves. Thus, differences between anticyclones characterized by either atmospheric blocking or temperature extremes or neither / both of them can be subsequently explored.With the help of Lagrangian backwards trajectories, a detailed view of the origin of near surface air masses within and in the proximity tracked anticyclones is obtained along their path and lifetime. Using a recently published temperature anomaly decomposition method, the physical processes leading to heatwaves (advection, diabatic and adiabatic heating) are explored and and compared to air masses in tracked anticyclones not associated with heatwaves.
Efficient adaptation of Foundation Models for Visual Grounding Remote Sensing task
Authors: Ghandour Ali J. , Moughnieh Hasan , Zahweh Mohammad Hasan , Nasrallah Hasan , Shukor Mustafa , Nattero Cristiano , Campanella Paolo
Published: March 2024

Foundation models have demonstrated impressive proficiency across multiple domains, including language, vision, and multi-modal applications, establishing new standards for efficiency and adaptability. In the context of localization-based foundational models, the core strength of such models is their ability to precisely recognize and locate objects across a diverse set of objects in wide-area scenes. This precision is particularly vital in the Remote Sensing (RS) field. The multimodality aspect of these models becomes pivotal in RS, as they can process and interpret complex data, allowing for more comprehensive aerial and satellite image analysis.Multimodality has emerged as a crucial and dynamic area in recent AI developments, finding diverse applications such as image captioning and visual question answering. More related to traditional visual tasks, Visual Grounding (VG) stands out, involving the localization of objects based on textual descriptions. Unlike conventional approaches that train models on predefined and fixed lists of objects, VG allows a model to locate any entity in an image based on diverse textual descriptions, enabling open-vocabulary predictions. Despite notable efforts in developing powerful VG models to solve general benchmarks, there is a need for more exploration into transferring these models to the remote sensing context.This paper addresses this gap by delving into the task of visual grounding for remote sensing. Our initial exploration reveals that utilizing general pretrained foundational models for RS yields suboptimal performance. After recognizing these limitations, our work systematically investigates various parameter-efficient tuning techniques to fine-tune these models for RS visual grounding applications. The insights and methodologies presented in this paper provide valuable guidance for researchers seeking to adapt pretrained models to the RS domain efficiently. This adaptation marks a substantial advancement in the field, offering a significant stride toward enhancing the applicability of visual grounding in remote sensing scenarios.
SeimicWaves.jl: an efficient yet user-friendly Julia package for Full-Waveform Inversion on multi-xPUs
Authors: Aloisi Giacomo , Zunino Andrea , Fichtner Andreas
Published: March 2024

This work focuses on creating efficient and user-friendly tools for seismic tomography using Full-Waveform Inversion (FWI) methods. FWI has proven effective in providing detailed images of the Earth's subsurface. Despite its success, challenges persist due to its high computational costs and complexity, limiting its widespread application in research and education.To fill the gap between theory and practice, we present efficient, easy-to-use, and scalable finite-difference-based solvers for FWI in the Julia programming language developed in the open-source package SeismicWaves.jl (part of HMCLab, a framework to perform Bayesian inversion and optimization for geophysical problems), which enable non-experts to conduct numerical experiments and address real applications with seismic data. Our device-agnostic solvers can be distributed on multiple devices (multi-xPUs), providing users with different parallelization options fitting diverse use cases.Rigorous tests and synthetic inversions validate the solvers' correctness, offering insights into both the potentials and pitfalls of the method. Benchmark tests evaluating memory throughput, crucial for the memory-bound algorithms under study, reveal that our solvers achieve high memory throughput (up to 90% of peak) on modern GPUs and exhibit good weak scaling on distributed systems.In conclusion, by leveraging advancements in software and hardware from the scientific computing community, our research addresses both computational and complexity challenges of FWI, making it a viable and efficient method for educational and research purposes in seismic tomography.
U-Pb calcite geochronology attests Late Cretaceous S-verging thrusting in the central Southern Alps, Italy
Authors: Zanchi Andrea , Zanchetta Stefano , Rocca Martina , Montemagni Chiara , Aldega Luca , Kylander-Clark Andrew , Fiorini Andrea , Carminati Eugenio
Published: March 2024

The central Southern Alps (cSA) form a complex south-verging polyphase fold-and-thrust belt developed from the Late Cretaceous onward due to the Alpine convergence (Schönborn, 1992; Zanchetta et al., 2015). The oldest ages related to thrust stacking have been obtained from pseudotachylythes along the Orobic Thrust exposed in the northern part of the belt (Zanchetta et al., 2011), stacking the Variscan basement onto the Permian-Triassic cover.Aim of this contribution is to present new U-Pb radiometric ages of calcite tectonites obtained in the central and southern portion of the belt, where no precise time constrains of thrust stacking are available. E-W trending Eocene dike swarms crosscutting thrust planes are the only indirect evidence of a pre-Eocene age of the oldest stages of the Alpine contraction (D’Adda et al., 2011).The central part of the cSA shows a thick pile of thrust sheets deforming the Lower to Middle Triassic carbonate successions. Our new U-Pb calcite ages obtained on growth fibers along fault planes, veins and calc-mylonites sampled along some of the most important regional thrust planes mainly result in Late Cretaceous ages. We obtained similar ages also within the southern portion of the belt, where the Norian “Dolomia Principale” thrust sheets, override the Rhaetian Riva di Solto Shale immediately to the north of the frontal portion of the belt. Younger ages resulted from the Paleogene units which are involved in the exposed frontal part of the belt, which is mostly buried under the recent infilling of the Po Plain forming the Milan Belt.These data confirm previous interpretations of the central Southern Alps as part of a Late Cretaceous doubly vergent pre-collisional belt (Zanchetta et al., 2012). Research supported by the FAST PRIN Project (2021-NAZ-0299, Italian MUR).D'Adda, P., Zanchi A., Bergomi M. A., Berra F., Malusà M. G., Tunesi A., & Zanchetta S. (2011) - Polyphase thrusting and dyke emplacement in the central Southern Alps (Northern Italy), International Journal of Earth Sciences, 100, 1095–1113.Schönborn, G. (1992) - Alpine tectonics and kinematic models of the central Southern Alps. Memorie di Scienze Geologiche, 44, 229-393.Zanchetta S., D'Adda P., Zanchi A., Barberini V. & Villa I.M. (2011) - Cretaceous-Eocene compressions in the central Southern Alps (N Italy) inferred from 40Ar/39Ar dating of pseudotachylytes along regional thrust faults. Journal of Geodynamics, 51, 245-263.Zanchetta, S., Garzanti, E., Doglioni, C., and Zanchi, A. (2012). The Alps in the Cretaceous: a doubly vergent pre-collisional orogen, Terra Nova, 24, 351–356, 2012.Zanchetta S., Malusà M. & Zanchi A. (2015) - Precollisional development and Cenozoic evolution of the Southalpine retrobelt (European Alps). Lithosphere, 7, 662-681.
Estimating Multi-sectoral Water Withdrawals Through Machine Learning for Attribution in an Ungauged Terminal Lake Basin in Central Asia
Authors: Deng Haoxin , Flörke Martina , Lei Kan , Tang Qiuhong
Published: March 2024

Many terminal lakes in Central Asia have witnessed concerning rates of shrinkage in recent decades. These lakes are particularly sensitive to both climate change and human water withdrawals. Although human water withdrawals are acknowledged as a major factor influencing long-term lake changes, previous studies often fail to distinguish the specific contributions of different sectors such as irrigation, livestock, industry, and domestic water usage. This knowledge gap is largely due to the absence of observed multi-sectoral water withdrawals. Recognizing the value of machine learning methods in predicting water withdrawals through complex, non-linear relationships between water uses and potential explanatory factors, we developed an innovative approach that integrates a hydrological model and a machine learning-based water use model. This methodology was applied to simulate the long-term changes in the area of Ebinur Lake, a large terminal lake in Central Asia. Water withdrawals estimated by Random Forest based on meteorological (temperature and precipitation) and socio-economic data (e.g., population, multi-sectoral GDP, per capita income, etc.) and allocated by irrigated area and population were extracted from the river route in the hydrological model which in turn affects the inflow into the lake. Finally, integrated model simulations were validated using remotely sensed lake areas and streamflow data from mountain hydrologic stations. Several experiments, including and excluding different sectoral water uses, were conducted to isolate factors influencing lake dynamics. The results indicated irrigation water withdrawal not only caused lake shrinkage, but also increased seasonal variability, thereby increasing the uncertainty of water supply to lake ecosystems. The proposed modelling approach provided a framework for quantifying the responses of terminal lake area changes to different sectoral water withdrawals in arid basins, especially in the absence of specific water withdrawal data.
Denoising DAS data in urban volcanic areas through a Deep Learning Approach
Authors: Allegra Martina , Cannavo Flavio , Corsaro Miriana , Currenti Gilda , Jousset Philippe , Palazzo Simone , Prestifilippo Michele , Spampinato Concetto
Published: March 2024

The notable benefits of Distributed Acoustic Sensing (DAS) technology—high coverage, high resolution, low cost—have led to its widespread application in the geophysical domain for high-quality data recording. Among possible applications, the ability to interrogate telecommunication cables has enabled the detection of a variety of seismic-volcanic events in poorly instrumented environments, such as densely populated urban areas.Nevertheless, the sensing of commercial fiber optic cables has to deal with the presence of anthropogenic noise that frequently corrupts the seismic signal. Indeed, vibrations induced directly or indirectly by anthropogenic activities significantly reduce the signal-to-noise ratio by masking target events.Taking advantage of the high spatiotemporal resolution of the DAS data, a deep learning approach has been adopted for noise removal. The architecture of the neural network together with the training strategy have enabled the extraction and preservation of salient information while neglecting anthropogenic noise.The validation on real low-frequency seismic events recorded during the 2021 Vulcano Island unrest  has provided encouraging results, demonstrating the potential of the proposed approach as a pre-processing step to facilitate subsequent DAS signal analysis.
Deciphering alpine stream responses to climate change: lessons from the METALP monitoring network
Authors: Deluigi Nicola , Robison Andrew L.
Published: March 2024

Streams and rivers are increasingly recognized as vital components of the global carbon cycle, especially in the context of climate change. To comprehensively understand their impact, it is essential to move beyond the study of individual reaches and consider the entirety of fluvial networks, including their terrestrial interactions. This holistic perspective is crucial for integrating fluvial networks into Earth System models and accurately assessing their role in the global carbon cycle.In this context, we describe the Metabolic Regimes in Alpine Stream Networks Program (METALP, https://metalp.epfl.ch), an ecohydrological and biogeochemical monitoring study of high-mountain streams in the Swiss Alps, running since 2016. Employing a network of high-frequency sensors (10-min) paired with monthly grab sampling, METALP examines the hydrological, thermal, light, and carbon regimes of high-mountain streams. Initially focused on the metabolism of alpine streams, the project has evolved to explore long-term trends in ecosystem characteristics and functions, with a particular emphasis on understanding climate change impacts. This unique observatory has so far collected over 20 million usable data points, describing annual regimes of streamwater flow, temperature, sediment load, carbon fluxes, and ecosystem metabolism.We present insights into the hydrologic and biogeochemical consequences of glacier loss, along with findings on dissolved organic carbon, gas exchange and CO2 emissions, oxygen concentrations, and gross primary production. Building on these insights, we then delve into the unique challenges associated with long-term monitoring in high-mountain catchments. These include marked hydrologic variability, with flows ranging over several orders of magnitude, and the need for monitoring equipment to withstand high flows, sediment loads, and avalanches, and remain functional during low flow periods. Seasonal snow cover and the remoteness complicate sampling campaigns and sensor maintenance. Additionally, the oligotrophic nature of high-mountain streams, with low analyte concentrations, necessitates sensitive monitoring programs capable of detecting subtle changes. These challenges inherently lead to gaps in data, necessitating not only technical adaptations for monitoring under difficult conditions but also innovative modeling strategies for compensating data loss.Finally, the METALP network, along with river networks located in different climatic regions (i.e., the Krycklan catchment in Sweden, the StreamPULSE project, or the Arctic Great Rivers Observatory), provides a broader perspective, enabling us to understand biogeochemical patterns and dynamics across multiple streams. This approach is crucial for constructing a comprehensive picture of stream biogeochemistry and its response to climate change.
Exploring links between the atmospheric water and trace element cycles in the Kara and Laptev Seas
Authors: Breuninger Esther S. , Thurnherr Iris , Tolu Julie , Aemisegger Franziska , Wernli Heini , Winkel Lenny H.E.
Published: March 2024

The atmosphere is an important reservoir for the essential elements selenium (Se) and sulfur (S) as well as for the toxic element arsenic (As). Atmospheric deposition is a source of these elements to terrestrial and marine environments, which can affect ecosystems and human health. The mobility and bioavailability of Se, S, and As in surface environments depend on their chemical forms (speciation). The factors that determine elemental speciation in atmospheric deposition are likely controlled by the speciation of these elements at the source (atmospheric emissions) and by their (bio)chemical transformations during transport. In addition, atmospheric transport of trace elements and their deposition patterns might be strongly linked to the atmospheric water cycle in particular cloud and precipitation formation, because wet deposition during precipitation is an important removal mechanism of trace elements from the atmosphere. To investigate the dynamical processes that govern the cycles of atmospheric water and trace elements in polar regions, including their sources, transport pathways, and sinks, we performed various chemical measurements (total element concentrations and speciation of Se, S and As) on atmospheric samples collected during the Arctic Century Expedition in the Kara and Laptev Seas (August-September 2021). Notably, trace element analyses were combined with a 4-week continuous time series of ship-based measurements of the isotopic composition of water vapour (i.e., δ2H and δ18O). Air parcel backward trajectories were used to identify atmospheric transport patterns of elemental and water isotope signatures, based on three-dimensional wind fields from the ERA5 atmospheric reanalysis dataset. Based on our chemical and meteorological observations and transport diagnostics, we present new insights into the variability of Se, S, and As concentration and speciation in atmospheric deposition and how they are linked to the atmospheric polar water cycle.
Midges’ marks – what chironomid head capsules tell us about Eemian and Early Vistulian palaeoenvironment – results from Bispingen , northern Germany
Authors: Polkowski Tomasz , Mroczkowska Agnieszka , Lauterbach Stefan , Neumann Frank , Tjallingii Rik , Słowiński Michał , Brauer Achim
Published: March 2024

Understanding the nature of past climatic changes is essential to assess human impact on current and future climate and environment. One of the means to achieve this goal is conducting past climate reconstructions based on subfossil Chironomidae remains. Using this method allows to estimate past summer temperatures and other palaeoecological parameters such as lake trophy and pH. Although Chironomidae analysis is very useful for reconstructing past climatic and ecological conditions, not many high resolution reconstructions have been conducted for periods prior to significant human impact, e.g. during the Eemian interglacial and Early Vistulian glacial. The present study is part of a larger project that applies a multi-proxy approach to the partially annually laminated sediments of the Eemian paleolake of Bispingen in northern Germany. The analysed sediment sequence consists of around 12 meters of diatomitic, calcitic-diatomitic and clastic-organic material and was sampled continuously at 2 cm resolution. The aim of this research is to reconstruct summer temperature changes throughout the Early Vistulian glacial and Eemian interglacial and draw conclusions with respect to the climatic gradient between northern and southern Europe. Chironomids’ head capsules analysis is supported by palaeoecological and palynological data, allowing to extract distinct palaeoecological phases on the basis of trophy and thermal conditions during the period considered. This project is funded by the Polish National Science Centre (No. 2019/34/E/ST10/00275).
Unexpected softening of a synthetic peridotite analogue (magnesium germanate) in a narrow temperature window: the Lithosphere-Asthenosphere Boundary accidentally reproduced?
Authors: Ferrand Thomas P. , Deldicque Damien
Published: March 2024

Seeking to better investigate the mechanism of transformational faulting (i.e. dynamic olivine phase transition considered as the main earthquake mechanism in the deep Upper Mantle), experiments were carried out in a 1-atm rig using synthetic samples of peridotite analogues. The samples consisted of Mg2GeO4 with minor MgGeO3. In brief, using Ge instead of Si allows studying deep processes without increasing the confining pressure. At 1 atm, γ-Mg2GeO4 (high-pressure olivine polymorph) transitions to α-Mg2GeO4 (olivine) at 810 °C. The hope was to generate and document strain localization features due to local phase transition and/or locally nucleate the phase transition due to strain localization. Working with this material in this apparatus between 760 and 860 °C could have appeared clever.The experimental setup allowed runs lasting a few hours up to a full day. However, after one day at 800 °C and an axial stress > 300 MPa, the synthetic samples, characterized by a small grain size and a high homogeneity, remained perfectly elastic. Runs were too cold and dry for any nucleation of the high-pressure phase γ-Mg2GeO4. Due to both kinetic and rheological issues, beginning to observe strain localization would have taken months, which would most probably have killed the apparatus in only one run due to the corrosion of the external furnace. This failed experimental journey turned into an opportunity to study something else: the temperature window was shifted to 950-1250°C. Some experiments revieled a significant viscosity reduction in a narrow temperature window (1000-1150°C), which we propose to interpret as an analogue of the lithosphere-Asthenosphere boundary (LAB). Until recently, melting was the only “transformation” considered in the interrogations about the reduced viscosity of the LAB. In this study, based on our unexpected experimental results, we document a solid-state viscosity reduction that seems to be associated with grain-boundary instability in the context of a competition between diffusive and displacive processes (i.e. premelting). We propose to broaden the discussion including solid-state transformations and potential metastable phases that are not yet fully understood. Although the most studied mineral, olivine has not revealed all its secrets. Additional experiments are required to fully understand what happened.
Optimizing Ammonia Volatilization Simulation in Agricultural Soils: Advancements of the EPIC Model
Authors: Gozio Andrea , Longo Matteo , Cabrera Miguel L. , Izaurralde Roberto César , Kissel David E. , Lazzaro Barbara , Dal Ferro Nicola , Morari Francesco
Published: March 2024

Agriculture is responsible for about 94% of UE ammonia (NH3) emissions, notably from livestock, manure management and soil fertilization. NH3 volatilization is a significant cause of reactive nitrogen (N) loss, leading to lower fertilizer efficiency as well as environmental and health concerns. Loss predictions can be estimated using process-based biogeochemical models, but many of them lack precise estimations of NH3 volatilization. In this work, we modified the Environmental Policy Integrated Climate (EPIC) model incorporating a mechanistic sub-model to simulate NH3 volatilization following the application of N fertilizers in agricultural fields. The newly added algorithm in EPIC functions on an hourly time step and describes the ammonium (NH4+) adsorption by clay and organic matter and estimates the partitioning of total ammoniacal N into NH3 and NH4+ based on the pH of the soil solution. The sub-model then determines the NH3 concentration in the gas phase using Henry’s law and estimates NH3 emission using a mass transfer coefficient that considers the resistance in the turbulent and laminar layers. Additionally, the sub-model uses the soil’s pH buffering capacity to recalculate pH following hydrogen ion consumption by urea hydrolysis and hydrogen ion release by NH3 volatilization. The sub-model further integrates a reduction factor for volatilization to account for the effects of soil layer depth and the depth of fertilizer application. The new EPIC sub-model was validated using datasets from Veneto, NE Italy, and Georgia, USA. In Italy, NH3 volatilization was measured in four experiments, testing cattle slurry, farmyard manure, and mixed silage maize and animal slurry digestate. Whereas in Georgia, NH3 volatilization was examined following surface application of urea and poultry manure to grasslands. The new sub-model improved NH3 loss prediction, yielding reasonable hourly NH3 fluxes and cumulative volatilization estimates. As a result, the EPIC model exhibited lower prediction errors for soil mineral N (e.g. NH4+and NO3-) dynamics. While the new sub-model marks a notable advancement in accurately modeling N cycling, additional enhancements should prioritize certain modeling aspects, including slurry infiltration rates, NH3 fluxes within the soil profile, and the mitigation effects resulting from urease inhibitor application.
AQUA: a novel quality assessment tool for km-scale simulations in the Destination Earth Climate Digital Twin - the core framework
Authors: Nurisso Matteo , von Hardenberg Jost , Caprioli Silvia , Ghosh Supriyo , Koldunov Nikolay , P. Kinoshita Bruno , Nazarova Natalia , Ghinassi Paolo , Davini Paolo
Published: March 2024

Destination Earth (DestinE) is a major initiative by the European Commission aiming to create a highly accurate global digital twin of Earth. The Climate Adaptation Digital Twin in DestinE is an ambitious project of several different climate simulations at the km-scale producing a large amount of heavy dataset, difficult to access and analyse with standard data processing  pipelines. Each project and each model produces data that may differ in format (NetCDF, GRIB, Zarr), structure and metadata, leading to the necessity of tweaks and complex pipelines in order to prepare data for analysis.We thus introduce AQUA, an Application for Quality assessment and Uncertainty quAntification. AQUA is composed of a core engine facilitating data access, combined with a series of modular and independent diagnostics to be run continuously to monitor and evaluate climate simulations. In this contribution we present the core engine and its features. Though many available suites already exist to analyse data from global climate models, AQUA has been specifically developed to deal with large km-scale datasets, with the goal of unifying and simplifying climate data access for all users. AQUA responds to the need for users to have the focus on the development of their data analysis, while datasets are found, retrieved and homogenised by an external tool to which they can connect their pipeline. Developed in Python, leveraging the power of Dask and Xarray libraries, AQUA prioritises efficiency through lazy data access. Noteworthy is the utilisation of cdo for one-time weight computation, enhancing performances in regridding and averaging operations. A key strength lies in its ability to handle high-resolution, high-frequency data, loading into memory only when necessary. AQUA not only unifies and simplifies climate data access for users but also addresses the crucial need for responsive feedback to climate model developers.
Paleoenvironmental changes in the Corinth Rift area (eastern Mediterranean) during MIS 1-5 based on benthic foraminifera and geochemical proxies
Authors: Koukousioura Olga , Puentes-Jorge Xabier , Panagiotopoulos Konstantinos , Diz Paula , Grunert Patrick
Published: March 2024

The Gulf of Corinth is a relatively young (
Crop model calibration at multiple spatial levels in southern Sweden using leaf area index, evapotranspiration and winter wheat yield data
Authors: Li Xueying , Zhang Wenxin , Wu Minchao , Zhou Hao , Olin Stefan , Bouras EI Houssaine , Thapa Shangharsha , Duan Zheng
Published: March 2024

Matching the rising demand for food from a rapidly increasing population is a crucial challenge for the 21st century. Crop yield can be strongly impacted by weather conditions, especially extreme events (e.g., floods and droughts). Therefore, understanding the spatial and temporal vaiations of crop yield enables us to develop effective adaptation strategies for enhancing the resilience of agriculture sectors under climate change.Dynamic global vegetation models (DGVMs) represent growth and development of vegetation based on the understanding of underlying physical and physiological processes, which are efficient tools to assess impacts of climate change and human management on vegetation response to these variations. During the past two decades, satellite observations have emerged as essential gridded datasets to calibrate various vegetation-related variables at large spatial scales, and are often used to benchmark DGVMs. Commonly used satellite-derived variables for model calibration include the fraction of absorbed photosynthetically active radiation, phenological dates, soil properties, leaf area index (LAI), and evapotranspiration (ET).One of the process-based DGVMs LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) has shown its acceptable performance in simulating crop yield at global and regional scales. However, to the best of our knowledge, no studies have comprehensively investigated the added value of using multi-source data, particularly satellite-derived estimates for calibrating LPJ-GUESS at multiple spatial scales. Therefore, we aim to bridge this gap by calibrating LPJ-GUESS at both site and district level to improve model performance of simulating the winter wheat yield in southern Sweden.First, all the parameters sensitive to LAI, ET and crop yield in LPJ-GUESS, along with their ranges, are derived from the literature and sensitivity analysis. Second, the LAI-related parameters are calibrated against the in-situ observed LAI at the experimental site in Alnarp during 2022–2023. The simulated yield based on the optimized parameters (achieved from calibration results) is further validated by the observed winter wheat yield at the same site. At the district level, the parameters for ET and crop yield are subsequently calibrated against the satellite-derived ET and crop yield estimates, and the observed district-level winter wheat yield (from Jordbruksverket), respectively during 2003–2012. Finally, the observed district-level yield during 2013–2022 are used for the model validation to access the performance of the calibrated LPJ-GUESS. We expect this proposed calibration framework can be applied to other DGVMs and geographic extents focusing on high details of local landscape.
Improving Earth System Models via Hierarchical System Development
Authors: Ek Michael ,   DTC HSD team
Published: March 2024

Hierarchical System Development (HSD) is an efficient way to effectively integrate the model development process, with the ability to test small elements (e.g., physics schemes) in an Earth System Model (ESM) first in isolation, then progressively connecting elements with increased coupling between ESM components. System in HSD is end-to-end: it includes data ingest/quality control, data assimilation, modeling, post-processing, and verification. HSD includes individual physics simulators, Single Column Models (SCMs; including “on/off switches” for individual physics elements), small-domain and regional models, all the way to complex fully-coupled global ESMs with atmosphere/chemistry/aerosol, ocean/wave/sea-ice, land-hydrology/snow/land-ice, and biogeochemical cycle/ecosystem components. Datasets used for the different HSD steps are obtained from observational networks and field programs, ESM output, or idealized conditions (e.g., used to “stress-test” ESM elements and components). Advancing from one HSD step to the next requires appropriate verification metrics of ESM performance, many at the process level. This process is concurrent and iterative such that more complex HSD steps can provide information to be used at simpler HSD steps and vice versa.  The HSD approach can also help understand spatial and temporal dependencies in model solutions, where consistency for different models and resolutions across HSD steps is required. The Common Community Physics Package (CCPP) is designed to lower the bar for community involvement in physics testing and development through increased interoperability, improved documentation, and continuous support to developers and users. Together, CCPP and its companion SCM, developed and supported by the Developmental Testbed Center (DTC), provide an enabling software infrastructure to connect HSD steps. The HSD approach and use of CCPP will be illustrated and discussed through testing and evaluation examples. This work also supports the NOAA Earth Prediction Innovation Center (EPIC) program.
From plumes to subduction network formation and supercontinent break-up
Authors: Pons Michaël , V. Sobolev Stephan , Jain Charitra , Kendall Elodie
Published: March 2024

The evolution of modern plate tectonics is described by the Wilson cycle, which portrays the dynamics of the supercontinental cycle through the interaction of the oceanic plate with the continental plate over periods of hundreds of millions of years. This cycle is characterized by a phase of supercontinent assembly and enhanced orogenic collision, followed by a phase of supercontinent fragmentation and dispersal, as shown by the geological record. The dynamics of the Wilson cycle is intrinsically linked to mantle convection and subduction dynamics. While the assembly phase appears to follow a degree-2 mantle convection style, the mechanism responsible for supercontinent fragmentation is still debated. We hypothesize that the dispersal phase is mostly governed by trench roll-back from subductions and mantle plumes. To test this hypothesis, we have built a series of 2D and 3D geodynamic models of the Earth on a global scale using the ASPECT code. We have tested different scenarios in which we prescribe the distribution of the supercontinent Rodinia at 1Ga or Pangea at 250 Ma and let the models evolve self-consistently.  In some model variants, the strength of the supercontinent and that of the surrounding oceanic area is changed. We will present our preliminary results and discuss the dynamics of continental dispersal and its link to subduction and mantle dynamics. In particular, 3D models will demonstrate how regional plume-induced retreating subduction zones evolve into a global network of subduction zones and tectonics plate boundaries which ultimately leads to the break-up of the supercontinent.
Cation Exchange Capacity Quantifies the Link Between Mineral Surface Chemistry and Frictional-Mechanical Behavior of Simulated Fault Gouges
Authors: Ikari Matt , Conin Marianne
Published: March 2024

The slip behavior of crustal faults is known to be controlled by the mineralogic composition of the fault gouge. The exact properties determining the frictional behavior of geologic materials, including diverse remains an important question. Here, we use a geochemical approach considering the role of water-rock interactions. As a mechanism, we suspect that the mineral surface charge allows attractive and repulsive forces (Van Der Waals type), and that those forces may influence the static mechanical behavior of clays (cohesion, static friction).  On the other hand, we suspect that the water bound to the mineral surfaces may play a role during shearing.  To address these ideas, we measured the cation exchange capacity (CEC) of 10 different rock and mineral types, including non-clays and a range of phyllosilicate minerals, using CEC as a proxy for the mineral surface charge and the ability to bind water to the mineral surfaces.  For these materials, we conducted laboratory shearing experiments measuring the pre-shear cohesion, peak friction coefficient, residual friction coefficient, post-shear cohesion, and velocity-dependent friction parameters under 10 MPa effective normal stress.  Our results show that low CEC materials (< 3 mEq/100g) tend to exhibit high friction, low cohesion, and show velocity-weakening frictional behavior. The phyllosilicate minerals exhibit larger CEC values up to 78 mEq/100g and correspondingly lower friction coefficients, higher cohesion, and velocity-strengthening frictional behavior. Zeolite exhibits a relatively high CEC value typical of phyllosilicates, but its strength and frictional characteristics are that of a non-clay with low CEC. This suggests that grain shape and contact asperity size may be more important for non-phyllosilicates. For phyllosilicates, we suggest that the systematic patterns in strength and frictional behavior as a function of CEC could be explained by water bound to the mineral surfaces, creating bridges of hydrogen or van der Waals bonds when the particles are in contact. Such bonding explains the large cohesion values for high-CEC materials under zero effective stress, whereas surface-bound water trapped between the particles under load explains low friction.  Beyond the results of this study, CEC appears to be a controlling factor for other properties such as permeability and even the amount of bound DNA in sediments. 
The exceptionality of the 2022 fire season over Central Europe
Authors: Gouveia Célia M. , Ramos Alexandre M. , Silva Mafalda C. , Durão Rita , Bayar A. Serkan , Pinto Joaquim G.
Published: March 2024

The frequency and intensity of temperature extremes have increased worldwide in the past couple of decades. The year 2018 was characterized by record-breaking temperatures in many parts of Europe during spring and summer, which lead to unusual and severe wildfires in central and northern Europe. For example, devastating fires destroyed large areas of intact forests, not only in countries with a long tradition of wildfires but also in countries, such as Sweden, Norway, Finland, and Latvia.  In 2022, Europe was characterized   by a prolonged spring drought, several summer heatwaves and fire activity without precedents for several European countries. In particular, a high number of fires occurred in Germany, Austria, Chechia, Hungary, Slovenia and Romania during the summer months, highlighting the increase of fire-prone conditions in the region linked with an increase of hot and dry conditions.This work analyses the exceptionality of the 2022 fire season over central Europe. Fire Radiate Power (FRP) from MODIS, burned area and number of fires from EFFIS were used to characterize fire occurrence in 2022. We used ERA5 meteorological parameters, such as the maximum and minimum air 2m temperature, minimum relative humidity, and wind speed to evaluate the severity of the heat extremes from April to August. SPEI for the time scales of 6 and 12 months were computed using ERA5 data to evaluate the drought conditions in spring and summer over the central European region. The Canadian Fire Weather Index (FWI) and sub-indices available from ERA5 data, were used to assess the exceptionality of meteorological fire danger over the region in the summer of 2022.  Moreover, possible FWI trends and sub-indices were also analysed for the period from 1979 to 2022. The impact of drought on vegetation productivity during the spring and summer of 2022 was also evaluated. Results highlight the new fire dynamics in Europe in recent years, with new emergent hot spots, in central and northern European countries. It is thus extremely important to assess of trends of fire danger and changes in fire activity over this region to better define the related activities of fire monitoring, as well as the definition of planning activities and suppression measures towards climate mitigation and adaptation.Acknowledgements: This study is partially supported by the European Union’s Horizon 2020 research project FirEUrisk (Grant Agreement no. 101003890) and by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020- IDL and DHEFEUS - 2022.09185.PTDC.