bluuebunny/crossref_metadata_2025 huggingface dataset:
Fold-and-thrust belt and foreland basin of the United Arab Emirates: Sedimentary structures, basement morphology, and tectono-stratigraphic evolution
Authors: Abdelmaksoud Ahmed , Ali Mohammed , Geng Meixia , Searle Michael
Published: February 2023

The subsurface structural geometries of the United Arab Emirates (UAE) fold-and-thrust belt (FTB) and foreland basins are interpreted from seismic, well data, and gravity and magnetic data, integrated with surface geology. We also determined the basement depths, as well as the relation between shallow sedimentary structures and deep basement features. Miocene to Lower Jurassic sequences were interpreted and mapped. Additionally, we outlined subsurface extent of Sumeini and Hawasina allochthonous nappes. The tectonic subsidence curves suggest that the final major passive margin rifting event occurred in the early Aalenian and lasted till Oxfordian. Loading of the Semail ophiolite thrust sheet and accompanying allochthonous thrust sheets resulted in uplift at ca. 95 Ma and rapid subsidence at ca. 83 Ma, indicating the transition of the Arabian margin from a rifted passive margin to a foreland basin. The region witnessed an accelerated subsidence during the late Oligocene-Miocene, attributed to the initial collision of the Central Iran and Arabian plates. The Permian-Jurassic NW-SE oriented rift faults were reactivated as thrust faults during the Late Cretaceous ophiolite obduction and late Oligocene-Miocene continental collision. Two different tectonic regimes are identified in the FTB. Based on the seismic reflection profiles and derivative maps of gravity and magnetic data, the northern regime has NNW–SSE striking thrusts, backthrusts and folds. It is characterized by major inversion of the rift faults with up to 3,700 m thrusting throw. Four major west-verging and east-dipping thrusts, which cross the northern area, form fault-propagation folds and dissect the entire stratigraphy. Whereas the southern regime around Jabal Hafit is dominated by the Tarabat backthrust, which cuts across the Upper Cretaceous and Cenozoic successions. Moreover, the Mesozoic platform carbonates are dissected by inverted faults (pop-up structure) in this regime. The Hawasina décollement, together with the inverted basement structures formed the Jabal Hafit anticline as a backthrust structure. On the other hand, results of the 3D gravity inversion indicate that the basement depths range from 11.5 km along uplifted areas of the FTB to 18.8 km within the deeper parts of the foreland basin. Significant northward increases in both basement depth and the thickness of the late Oligocene-early Miocene successions indicate a northward increase in the severity of thrusting and crustal loading along the main Khusub thrust. The basement uplifts are found to be correlated with shallower anticlinal features, indicating a direct relationship between basement uplifts and shallow sedimentary structures and hence indicating reactivation of basement structures during the Zagros collision.
Phthalate esters in atmospheric PM2.5 in Bhopal, central India: Identification, Concentrations, and Health Risk Assessment
Authors: Yadav Kajal , Sunder Raman Ramya
Published: February 2023

Phthalate esters (PEs) are a group of synthetic organic compounds that are frequently employed as plasticizers, additives in the production of plastic products, as well as in cosmetics, personal care items, medical products, and insecticides. Since PEs are not chemically linked to polymers, they can easily be released into the environment both during production and use, as well as following the disposal of plastic items. These compounds  also belong to the class of endocrine disrupting species and have gained research attention due to their widespread environmental occurrence and associations with respiratory system diseases, and higher incidence of allergies. However, very little is known regarding the inhalation exposure to endocrine disrupting PEs. In this study an analytical approach- thermal desorption gas chromatography coupled with mass spectrometry (TD-GC/MS) was optimized for the measurement of particle-bound PEs. We report monthly (Jan, Feb, Mar, and Apr, 2019) concentrations of 6 PEs, viz., Dimethyl Phthalate (DMP), Diethyl Phthalate (DEP), Di-N-Butyl Phthalate (DnBP), Butyl Benzyl Phthalate (BBZP), Bis(2-Ethylhexyl) Phthalate (DEHP), Di-N-Octyl Phthalate (DNOP) measured every other day in ambient PM2.5 over Bhopal – one of the regionally representative sites of the COALESCE network (Lekinwala et al., 2020).Two endocrine disrupting compounds – DEHP and DBP exhibited high concentrations during  the months of Jan (23.6 ± 7.1 ng m-3 and 12.3 ± 4.2 ng m-3) and Feb (21.3 ± 10 ng m-3 and 11.0 ± 3.5 ng m-3). The average daily intake of Σ6 PEs and DEHP via inhalation were 0.11-0.21 μg/Kg day and 0.01-0.1 μg/Kg day, respectively, for adults in Bhopal. The inhalation cancer risk metric revealed that the estimated exposure to DEHP (3.9 × 10-5) exceeded the acceptable risk threshold. These results provide critical information that suggests that PEs from ambient and indoor sources should be considered when exploring the inhalation health risks to PM exposure.
Diagnosing stirring in the Arabian Sea from a long-term dataset
Authors: Paul Nihar , Sukhatme Jai , Mathur Manikandan , Sengupta Debasis
Published: February 2023

Stirring of passive tracers on the surface of the Arabian Sea is studied from a long-term data set comprising AVISO surface geostrophic and Globcurrent on a sub-seasonal and interannual time scale. To begin with, we elucidate the spatiotemporal characterization of surface flow and sea-surface salinity. We then use lagrangian metrics such as Finite-Time Lyapunov exponents (Finite-Size Lyapunov exponents) to characterize the chaotic mixing and its probability density function on a sub-monthly timescale over the basin. Further, the M-function is used to identify and showcase the lagrangian barrier associated with mesoscale eddies and its role in transporting freshwater from the Bay of Bengal to the Arabian Sea during the northeast-monsoon period (November to February).
Experimental study of CO2/CH4 distribution in shale rock samples during adsorption/desorption reaction by low-field NMR
Authors: Ahn Taewoong , Seo Young-ju , Park Changhyup , Lim hyunjoong , Kim Dong Hyun
Published: February 2023

Shale is attracting more attention than ever because it can act as a cap rock for CO2 storage as well as a source rock for hydrocarbon resources known as shale gas. In particular, it has been known that the enhanced gas recovery (EGR) technology that enhances the recovery of CH4 by injecting CO2 can be applied to shale gas production. Gas in shale is known to exist in phases of free gas and adsorbed gas, and the adsorption tendency of CO2 is higher than that of CH4. Because of these unique characteristics, CO2 injected into shale induces desorption of CH4 (natural gas production) and remains in adsorbed phase (CO2 storage) at the same time. In other words, shale can also serve as a CO2 storage site. Since shale has a complicated pore structure and a very small pore size, research on the fluid flow or CO2-CH4 adsorption-desorption mechanism within shale has not been well investigated yet.In this experimental study, Low-field NMR was used to analyze the characteristics of NMR signals of gases present in shale and how they change according to various gas pressures. In addition, the CO2-CH4 adsorption-desorption mechanism was analyzed by observing how the signal characteristics due to adsorption and desorption change as CO2 was injected into a shale sample saturated with CH4 gas. Through this study, it was confirmed that the NMR signal obtained from shale sufficiently reflects the phase and amount of gas, and that the progress of the adsorption-desorption reaction can be quantitatively analyzed. The results of this experiment can be used as important analytical data to understand the behavior of gas in shale, which is essential for shale gas recovery enhancement and CO2 storage.
A robust deep learning-based active fire detection model in diverse environments by fusion of satellite and numerical model data
Authors: Sung Taejun , Kang Yoojin , Im Jungho
Published: February 2023

Due to the irregular and sporadic nature of wildfires, continuous monitoring of large areas is required. Since geostationary satellite sensors can observe large areas with high temporal resolution, they are suitable for monitoring wildfires in real time. However, the threshold algorithm currently employed for satellite-based active fire detection has poor performance in sensors with low spatial resolution. In addition, the algorithm does not account for environmental conditions that affect wildfire detection, resulting in poor generalization performance for large areas. This study examines the viability of an adaptive active fire detection model by combining satellite and numerical model data with deep learning. A model for active fire detection was developed using commonly employed brightness temperature-related variables (key variables) and local environmental variables (sub variables). Key variables are the cross spectral and spatial differences between the MIR (central wavelength of 3.85 m) and 2 TIR (central wavelengths of 9.63 and 11.20 m) channels of the Advanced Himawari Imager (AHI). Sub variables include Solar zenith angle (SOZ) and satellite zenith angle (SAZ) of AHI, skin temperature (ST) and relative humidity (RH) of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5)-land data. Four processes (confidence, frequency, land cover, and continuity tests) were used to extract reference fire samples from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire products. To consider the different properties of key and sub variables, a 2-way convolutional neural network (CNN) structure was developed. To evaluate the influence of environmental variables, a CNN model without sub variables was adopted as a control model. The 2-way CNN (recall of 0.86, precision of 0.96, and standard deviation of recall of 0.13) was more robust at five focus sites than the control CNN (recall of 0.82, precision of 0.97, and standard deviation of recall of 0.163). Despite having a lower spatial resolution than MODIS/VIIRS, 2-way CNN outperformed other satellite-based active fire products (MODIS, VIIRS, AHI, and Advanced Meteorological Imager) in terms of detection capacity. The control CNN demonstrated poor performance under certain environmental conditions (high RH, high SAZ, and transition time between day and night), but 2-way CNN mitigates this tendency. In particular, the use of RH improved detection sensitivity, and SAZ contributed to the spatial robustness. This study demonstrated the significance of environmental conditions in active fire detection and proposed a suitable CNN structure for this intent. Based on the findings of this study, higher-level adaptive active fire monitoring under diverse environmental conditions will be possible together with explainable artificial intelligence.
Narrowing uncertainties in projected warming by constraining using the past global warming trend with the pattern effect removed
Authors: liang yongxiao , Gillett Nathan , Monahan Adam
Published: February 2023

Observational constraint methods based on emergent relationships between observable predictors and future projected warming across multi-model ensembles enable us to constrain multi-model projections. Unforced internal variability in predictors can weaken such emergent relationships. Assessing the Sixth Coupled Model Intercomparison Project (CMIP6) with all accessible realizations, we find that there of sea surface temperature (SST) trend over the eastern tropical pacific (ETP) which is well correlated with the global warming trend. The strong cooling in the ETP in observations induces a global-scale cooling, yet most realizations in the CMIP6 multi-model ensemble cannot reproduce it. Using the observed raw historical global mean near surface air temperature (GSAT) trend as a constraint therefore results in a relatively lower projected 21st century warming. However, by removing the unforced internal variability associated with variation in the ETP in observed and simulated GSAT trends, we find an enhanced correlation between GSAT trends and projected warming and improved results in an imperfect model test. This approach results in a relatively higher 21st century warming than a constrained projection based on the raw GSAT trend, and brings constrained projections into much closer agreement with projections constrained using climatological cloud metrics.
From the tongue of the Mer de Glace to the world’s glaciers : 20 years of progress in measuring glacier mass changes from space
Authors: Berthier Etienne , Belart Joaquin , Blazquez Alejandro , Brun Fanny , Deschamps-Berger Cesar , Dussaillant Ines , Flament Thomas , Hugonnet Romain
Published: February 2023

In 2004, we painstakingly measured the thinning of a single glacier tongue (the Mer de Glace, Mont-Blanc) from pairs of SPOT (CNES) satellite optical stereo-images. It then took us nearly 20 years before we managed to up-scale such observations to the global scale. In this presentation, I will illustrate the advances (in terms of data availability and processing) and all the collaborative work that led to a spatially-resolved and almost complete estimation of mass changes for the more than 200,000 glaciers on Earth. These new data paint a global picture of accelerating glacier mass loss since 2000 and pave the way toward improved projections of future glacier mass and sea level contribution.
Seamount subduction and megathrust seismicity: the interplay between geometry and friction.
Authors: Menichelli Irene , Corbi Fabio , Brizzi Silvia , van Rijsingen Elenora , Funiciello Francesca , Lallemand Serge
Published: February 2023

It has been widely recognized that the presence of seamounts can profoundly affect megathrust seismicity. With their outstanding topography, seamounts can tune interplate stress and favor the development of a fracture network in the overriding plate. Subducting seamounts can also control fluid accumulation and sediment porosity. However, their role as barriers or triggers for rupture propagation remains a matter of debate.In this work, we used analog models to study how geometric and frictional heterogeneities associated with a single subducting seamount influence the seismogenic behavior of the megathrust. We used four different model configurations (i.e., a flat interface, a high-friction and low-friction seamount, and a low-friction patch) to investigate both the combined and individual effect of geometry and friction.Our results show that low friction areas, either flat or with a seamount relief, reduce interplate coupling. Also the presence of a geometric feature tends to decrease seismic coupling and segment ruptures promoting earthquakes enucleation on the flat region. The maximum barrier efficiency is achieved with the low-friction patch model, where the accumulated stress is preferentially released by the occurrence of small earthquakes. This behavior is well suited to natural cases where seamounts are supposed to lower interplate friction due to fluid release or by the development of fracture systems development, causing microseismicity and slow slip events.
Four alternative ways to identify compound climate extremes and their relevance to ecological impacts: a case study of UK butterflies
Authors: Shan Baoying , De Baets Bernard , E.C. Verhoest Niko
Published: February 2023

Given observed and predicted increases in the frequency and intensity of many climate extremes, researchers have shown an increased interest in the climate extremes and their impacts on ecosystems because of the profound effects. However, most previous studies on the responses of ecosystems to climate extremes focus on droughts and summer heatwaves, and relatively little is known about the effects of other kinds of extremes, such as winter heatwaves, extreme wet periods, and cold waves.In this study, we identify four types of extremes (two temperature (heatwaves and cold waves) and two precipitation ones (droughts and extreme wet periods)) and present 4 alternatives to identify compound extreme events. We demonstrate the relevance of the different types of year-round (compound) events for ecological studies by demonstrating their impact on the abundance of 34 UK butterfly species across each species' life stages (hibernation, egg, larval, pupal, and adult) over a 45-year period. We chose this example as these species are expected to respond rapidly to climates due to their ectothermic nature and short life cycles.The results show that considering different types of year-round (compound) extreme events is relevant from an ecological point of view as at different stages, other extremes have more impact on the survival of individuals. For instance, statistics show that heatwaves and droughts during the pupal and adult stages appear beneficial for butterflies in England, with around 30% of univoltine species showing significant positive influences, whereas extreme wet periods during the pupal life stage cause negative population change for 26% of univoltine species. Our study demonstrates that considering different forms of extremes during all seasons of a year may bring interesting new insights for ecologists. However, we did not seek any eco(fysio)logical explanations of the obtained results.
Counteracting sensitivity accumulation near source and receiver locations in 3D inversion of controlled-source electromagnetic data
Authors: Rulff Paula , Kalscheuer Thomas , Zbinden Dominik
Published: February 2023

We present a synthetic inversion study illustrating two approaches which help to deal with heterogeneous sensitivities in 3D frequency-domain controlled-source electromagnetic inverse problems.Using edge-based vector finite-element approximations on tetrahedral meshes and a preconditioned non-linear conjugate gradient algorithm, we invert for impedance tensor elements generated by a set of two coincident perpendicularly oriented horizontal electric or horizontal magnetic dipole sources. Depending on the number and locations of sources and the choice of impedance tensor components used for inversion, the sensitivity patterns can differ significantly. Measurement setups with a small number of sources, but many receiver stations at the surface covering near-field, transition zone and far-field, are often deployed for land-based controlled-source electromagnetic measurements. Such a setup can result in accumulated sensitivities close to the sources and receivers, which implies strongest model updates in these regions and can mislead the inverse algorithm to a search direction, where no physically meaningful model can be produced nor the data are fitted. In order to mitigate the influence of strong sensitivities near sources and receivers on the inversion process, we apply an efficient preconditioner and customisable weights in the model regularisation matrix. The preconditioner is updated with the Broyden-Fletcher-Goldfarb-Shanno algorithm using the diagonal of the approximate Hessian matrix as start preconditioner. The latter is computationally expensive to obtain, but aims at finding a favourable search direction for the inverse algorithm already in early iterations and distributing the model update more evenly in the domain. To account for the sensitivity loss with depth, we implemented a depth weighting functional in the model regularisation term. The approach is based on counteracting the exponential and geometrical decay of the electromagnetic fields with depth and distance from the sources. In practical, we increase the smoothing in the shallow part of the model close to the source locations, where no structure is expected. We present synthetic examples indicating that this approach is an efficient way of helping the inversion to converge, obtaining a reliable model and resolving structure at depth.
Accounting for systemic complexity in the assessment of climate risk
Authors: Zscheischler Jakob , Westra Seth
Published: February 2023

Widespread changes to climate-sensitive systems are placing increased demands on risk assessments as a key for managing climate risk, enabling adaptive responses and enhancing system resilience. Although the complex, uncertain and ambiguous nature of climate-sensitive systems has been long recognised, recent attention on concepts such as compounding and cascading risks, deep uncertainty and ‘bottom-up’ risk assessment frameworks have stressed the need to more explicitly confront the overarching theme of systemic complexity. Drawing on insights from the field of systems thinking, we provide a theoretical foundation for addressing systemic complexity when assessing climate risks. We first describe the sources of systemic complexity as they pertain to climate risk, and highlight the role of climate risk assessment as a formal sense-making device that enables learning and the organisation of knowledge of the interplay between the climate-sensitive system and its (climatological) environment. We then highlight boundary judgements as one of the core concerns of risk assessment, acting as a filter of both information and value judgements, and thereby creating islands of analytical and cognitive tractability in a complex, uncertain and ambiguous world. Yet boundary judgements necessarily result in partiality, leading to the need for boundary critique, which emphasises the need of multi-methodologies and second-order learning processes as part of standard risk assessment practice. We build these concepts into a framework that divides climate risk assessments into five distinct but interrelated concerns or ‘problematics’ that collectively can be used as a starting point for managing systemic complexity in the assessment of climate risk. 
Design and construction of a rainfall simulator: an interdisciplinary student project towards sustainable development goals achievement
Authors: Klun Mateja , Lebar Klaudija , Zabret Katarina , Zdešar Andrej
Published: February 2023

Precipitation is one of the essential parts of the hydrological cycle. Regardless of the important role that precipitation plays for life on Earth, in extreme conditions, such as high intensities and amounts, it can negatively affect various ecosystem services (e.g., flood protection, agriculture). Precipitation is spatially highly variable. Traditionally, precipitation, with rain as the most common type, is measured over very small surface areas (of a few square decimetres) by rain gauges or sensors. However, reliable and representative rainfall data are crucial for understanding the interconnection of different parts of the hydrological cycle (e.g., rainfall interception by vegetation, rainfall erosivity) and quantification of flood, drought, water quality, and other water-related problems. Reducing the negative consequences of the mentioned problems is part of the 2030 Agenda sustainable development goals. Therefore, an interdisciplinary student project on the design and construction of a rainfall simulator was submitted to the University of Ljubljana's call for sustainable development student projects. Rainfall simulators are recognized as important tools for studying the effects of rain on soil. Rainfall simulators can be used in controlled conditions in the laboratory or with additional settings also in the field. The design and construction of the simulator is entirely within the domain of the project team of six students of environmental civil engineering and electrical engineering. This includes the choice of pipe materials, pump capacity, size and type of spray nozzles, development of a control system for monitoring and recording of results, and, last but not least, the determination of rain properties we would like to simulate (e.g., intensity). Three pedagogical mentors and one mentor outside the academia supervise the project. With such a project, group work and co-creation are encouraged among students, theoretical knowledge acquired within the curriculum is transferred into practice and knowledge is exchanged between different disciplines. Skills such as communication, critical thinking, organization of tasks and time management, interdisciplinary problem solving, analytical reasoning, information and technology literacy, are developed in the project. Additionally, such equipment will be used for teaching and research purposes in the future, which is another sustainable feature of this project.
Toward prediction of land subsidence assisted by artificial intelligence approaches
Authors: Shokri Nima , Mahdavi Ara Mehdi , Ansari Sobhan , Sharifi Mohammad
Published: February 2023

Land subsidence referring to the lowering of Earth’s land surface poses destructive threats to buildings and infrastructures and increases vulnerability to floods. The tendency to overexploit groundwater resources due to ever-increasing demand for water in urban areas is known as one of the main drivers for land subsidence, especially in regions with compressible sediments or formations susceptible to changes in groundwater pressure. Land subsidence has been observed in many countries around the globe including but not limited to USA, Mexico, Spain, Italy, Saudi Arabia, Iran, India, Vietnam, and China.Artificial intelligence (AI) and machine learning algorithms prove to be of great values to assess and predict a variety of hydrological and environmental dynamics and trends (Hassani et al., 2021; Mahdaviara et al., 2022). Leveraging on this opportunity, we develop a new framework, assisted by AI approaches, to quantify and predict how various environmental and climatic parameters influence the occurrence and extent of land subsidence. We show the general applicability of the proposed framework through the case of land subsidence observed in Iran, i.e. a semi-arid to arid country which strongly relies on the limited groundwater resources for a wide range of activities. The country hosts some of the fastest-sinking cities in the world. As a case study, we focused on the land subsidence observed in Varamin plain located in central Iran with an average annual precipitation of 420 millimeters and 210 millimeters of subsidence per year in the last 20 years.  A combination of the field and satellite data over the last two decades was prepared for the training of the models. In the next level, the training matrix was exposed to the AI algorithms aiming to develop models relating the land subsidence rate to a variety of environmental and climatic factors. Our preliminary analysis suggests that the groundwater withdrawal and precipitation rate are among the most important parameters affecting the rate of subsidence. The modelling tools will be used to detect the potential hotspots for land subsidence under different water management and climate change scenarios in other places. This will be helpful for preventing the forthcoming damages and devising the necessary action plans to mitigate the situation under different conditions. ReferencesHassani, A., Azapagic, A., Shokri, N. (2021). Global Predictions of Primary Soil Salinization Under Changing Climate in the 21st Century, Nat. Commun., 12, 6663. doi.org/10.1038/s41467-021-26907-3.Mahdaviara, M., Sharifi, M., Bakhshian, B., Shokri, N. (2022), Prediction of Spontaneous Imbibition in Porous Media Using Deep and Ensemble Learning Techniques, Fuel, 329, 125349.
Changes in 90Sr transport dynamics in groundwater after large-scale groundwater drawdown in the vicinity of the cooling pond at the Chornobyl Nuclear Power Plant
Authors: Sato Hikaru , Shibasaki Naoaki , Gusyev Maksym , Onda Yuichi , Veremenko Dmytro
Published: February 2023

Migration of long-lived radioactive 90Sr introduced by nuclear accidents and radioactive waste requires long-term monitoring and protection management due to its half-life of 28.8 years and high mobility in water. Presently, 37 years have passed since the largest worldwide 90Sr contamination was released and deposited around the Chornobyl Nuclear Power Plant (ChNPP). In the vicinity of the ChNPP, the water level of the cooling pond (CP) has declined since May 2014 following the decommissioning phase of the Unit 3 reactor. The drawdown of the CP lowered the groundwater level in a massive vicinity (about 70 km2), and the change in the groundwater system due to the drawdown has caused concerns about possible changes in 90Sr concentrations in water and transport dynamics to the Pripyat River. Therefore, this study evaluated how 90Sr transport dynamics were influenced due to changes in the groundwater flow system from 2011 to 2020 based on observed data and results of the groundwater flow simulation in the CP vicinity.The numerical simulation was conducted from 2011 to 2020 on monthly time-step using USGS MODFLOW with PM11 GUI and calibrated to groundwater heads measured at monitoring wells. In the location between the CP and the Pripyat River, estimated pore velocities near the river were reduced compared to velocities before the CP drawdown due to the decrease in the hydraulic gradient between the CP and the river. Decrease in groundwater velocity results decrease in groundwater discharge and delay of 90Sr transport. Therefore, the amount of 90Sr transported from the CP to the river is smaller than the period prior to the CP drawdown. The reduced 90Sr transport is expected to have less impact on the radioactivity in the river water even in the Pripyat River floodplain northwest of the CP where 90Sr concentrations significantly increased after the CP drawdown. In addition, the measured and simulated changes in groundwater flow direction and velocity suggested the possibility of 90Sr accumulation at the floodplain caused by stagnant groundwater from reduced velocity and additional 90Sr infiltration from surrounding ponds located at the Pripyat River floodplain. Therefore, enhancing the current monitoring of 90Sr concentrations near the floodplain would be needed for long-term monitoring and protection management to prevent the risk.
Summer Lake Destratification Phenomenon: A Peculiar Deep Lake on the Tibetan Plateau
Authors: Su Rongmingzhu , Ma Weiqiang , Xie Zhipeng , Wang Binbin , Hu Wei , Su Zhongbo
Published: February 2023

Lake water temperature and the related thermal structure influence not only the provision of ecosystem services in lacustrine environments but also the interactions with regional climate. However, continuous lake temperature monitoring across the Tibetan Plateau is sparse, limiting our understanding of lake thermal and mixing dynamics and hindering the verification of modeling results in this region. Based on in-situ water temperature and meteorological observations, this study revealed a special summer destratification phenomenon of a deep alpine lake on the Tibetan Plateau, Langa Co. The results indicate that Langa Co is a discontinuous cold polymictic lake, which becomes completely mixed and reaches a homogeneous water temperature frequently during spring and autumn. Further, the intermittent periods of stratification in summer only last a few days, which is rare for such a deep lake (mean depth = 22 m; maximum depth = 49 m). As an example of a discontinuous cold polymictic lake that contrasts with the typical dimictic pattern of alpine lakes, studies of Langa Co help to gain insights into lake thermal processes and thermoregulation mechanisms and establish a reference for lake model evaluation and parameterization on the Tibetan Plateau.
Software infrastructure development on the LUMI supercomputer to deploy the next generation Earth system models
Authors: Devaraju Narayanappa , Manninen Pekka , Enkovaara Jussi , Nortamo Henrik , Klocke Daniel , Kontkanen Jenni , Acosta Mario
Published: February 2023

The Destination Earth (DestinE) climate adaptation digital twin (climate DT) will be run on the EuroHPC supercomputers LUMI and Marenostrum. The climate DT project brings together leading centers from across Europe specializing in climate science and services, Earth system modelling, and supercomputing to deliver an innovative climate information system through DestinE supporting the European Union’s climate adaptation efforts. Usage of the Earth-system model simulations requires an orchestration of a number of software tools that are developed and implemented as part of the climate DT project. Here, we present the progress of the software infrastructure development and support on LUMI to deploy the next generation Earth system models ICON (Icosahedral Nonhydrostatic) and IFS (Integrated Forecasting System) coupled with the ocean models NEMO (Nucleus for European Modelling of the Ocean) and FESOM2 (Finite-volumE Sea ice-Ocean Model). Our priority is to provide the computing environment needed on LUMI for (i) the development and execution of the climate models, impact models and data analysis utilities, (ii) the infrastructure components needed in terms of data storage, discovery, and quality control both internally and towards a link with the DestinE data services, and (iii) the software development tools and optimization support during the development of the climate models to ensure LUMI is efficiently used to reach the desired throughput. Several software tools have been deployed on LUMI successfully, for instance, MultIO the I/O server to execute the climate models outputs, FDB (Fields Data Base) for the data storage and access, Autosubmit and ecFlow for orchestration of workflows on LUMI which configures, executes, and monitors the climate DT experiments. Further, GPU adaptation of ICON and IFS-NEMO/FESOM2 models on LUMI-G (GPU) is undertaken. At the moment IFS and ICON compile and run successfully on LUMI-C (CPU). Profiling and performance analysis of the Earth system models (IFS-NEMO/FESOM2 on LUMI-C and ICON on LUMI-G) will be presented with throughput and performance numbers for the baseline test case configurations. Issues and challenges will be discussed in detail in the presentation.  
Eddy-rich Arctic as future Sea ice disappears in high-resolution view
Authors: Li Xinyue , Wang Qiang , Koldunov Nikolay , Danilov Sergey , Jung Thomas , Müller Vasco
Published: February 2023

The continuing retreat of sea ice affects the Arctic mesoscale eddies, and its future evolution will strongly influence air-sea-ice interactions. However, knowledge of eddy activity is limited to sparse observations and coarse resolution models. How future eddies and their effects will evolve remains uncertain. Here, we apply the global unstructured model FESOM2 for 143 years of 4.5 km-Arctic simulations up to 2100 and 1 km-Arctic simulations for 5 years from 2010; 2090 to reveal the interactions between eddies, winds, sea ice and the energy budget of eddy kinetic energy (EKE) in a high resolution view. We demonstrate a significant increase in future Arctic EKE from 0-200 m, which is stronger in summer when sea ice melts. The future abundance of EKE can be explained by an increase in winter eddy generation and a decrease in summer eddy dissipation. This also leads to an enhancement of the horizontal velocity field, thus filling the Arctic Ocean with eddies in the future.
Meridional Connectivity of a 25-year Observational AMOC Record at 47°N
Authors: Wett Simon , Rhein Monika , Kieke Dagmar , Mertens Christian , Moritz Martin
Published: February 2023

The Atlantic Meridional Overturning Circulation (AMOC) plays a key role for the climate system of Europe and the Arctic by redistributing heat and freshwater in the Atlantic. Since climate model studies project a likely decline of the AMOC under climate change in the 21st century, monitoring AMOC changes remains an important task. Several moored arrays in the Atlantic deliver estimates of the AMOC volume transport. The longest of these observational AMOC records is the RAPID array in the subtropical North Atlantic. The depiction of the AMOC as a global ocean conveyor assumes that the AMOC variability is consistent across latitudes. This concept has been questioned by model studies. However, model studies and estimates based on altimetry and Argo data disagree on the regions and timescales of meridional connectivity. From measurements of the North Atlantic Changes (NOAC) array in the subpolar North Atlantic at 47°N we calculate the AMOC volume transport timeseries. Our approach combines data from moored instruments with hydrography (from Argo floats and shipboard measurements) and satellite altimetry. Here, we present this 25-year (1993-2018) purely observational AMOC record in monthly resolution and analyze its meridional connectivity with the subtropical RAPID AMOC.
Solar Wind Energetic Neutral Atom Observation at Mars by MINPA Onboard the Tianwen-1 Orbiter
Authors: Ma Jijie , Li Wenya , Kong Linggao , Zhang Yiteng , Wurz Peter , Galli André , Tang Bingbing , Xie Lianghai , Wang Limin , Qiao Fuhao , Li Lei , Wang Chi
Published: February 2023

The Mars Ion and Neutral Particle Analyzer (MINPA), one of the three scientific payloads onboard the Tianwen-1 orbiter, was designed to measure ions and energetic neutral atoms (ENAs) at Mars. From November 2021, MINPA started to collect scientific data around Mars. Here, we present MINPA's first results of the solar-wind ENAs, which are produced through the charge exchange process between the solar wind hydrogen ions and the Martian neutral exosphere. We perform a comprehensive comparison between the inflight ENA data and ground calibration results to understand the energy and angular distributions of the solar-wind ENA signals. The possible contamination of these observations by ions and solar extreme ultraviolet (EUV) is evaluated by comparing the ENA measurements with the ion data. We will present several cases of the solar wind ENA observations, and their intensities are estimated to be 10^5~10^6 cm^-2 sr^-1 s^-1, which is in good agreement with previous in situ measurements and predictions using models.
Nightside plasmaspheric plume-to-core migration of whistler-mode hiss waves
Authors: Wu Zhiyong , Su Zhenpeng , Goldstein Jerry , Liu Nigang , He Zhaoguo , Zheng Huinan , Wang Yuming
Published: February 2023

Whistler-mode hiss waves play an important role in the radiation belt electron depletion. Whether the hiss waves with significant differences in amplitude and propagation direction within the plasmaspheric core and plume are related to each other remains unclear. We here show that the plasmaspheric plume facilitates the energy conversion from energetic electrons to hiss waves and then guides hiss waves into the plasmaspheric core. Three ground and space missions captured the initial formation and subsequent rotation of the plasmaspheric plume in the noon-dusk-midnight sector following a strong substorm. The observed hiss waves in the nightside plasmaspheric plume and core propagated oppositely but highly correlated with each other at a time lag of 4-10 s. The linear instability of energetic electrons in the plasmaspheric plume qualitatively explains the frequency-dependence of hiss waves, and the ray-tracing modeling reproduces the propagation direction and timing of hiss waves.
Effect of inter row management systems on organic carbon and soil physical soil health parameters in vineyards.
Authors: Liebhard Gunther , Strohmeier Stefan , Toth Marton , Strauss Peter
Published: February 2023

Inter rows in vineyards provide several functions, which may compete with each other. Thus, different strategies for maintaining inter rows are commonly applied, particularly in vineyards. On the one hand, greening in vineyard inter rows, with no or occasional tillage operations, improves soil structure and health through a range of effects from erosion protection to carbon sequestration. On the other hand, tillage of inter rows is applied to fulfil diverse functions ranging from weed and pest control to the prevention of water competition and water loss during episodes of droughts. However, this may affect soil health and structure parameters regulating essential soil functions.This study investigated the effects of tillage intensity and green cover on soil organic carbon content and particular soil physical health parameters. 16 vineyards at eight sites in eastern Austria were sampled for bulk density, pore size distribution, percolation stability and soil hydraulic parameters in topsoil samples from 3 to 8 cm. For each site, the parameters were compared for a vineyard with high and a vineyard with low intensive managed inter rows.Comparing high and low intensive managed inter rows showed that soil health parameters were generally better in vineyards with low intense management and green covers. In particular, soil organic carbon and percolation stability were significantly higher under low intensive soil management with a green cover. Soil bulk density and macropore flow were significantly higher under intensive tillage. However, no significant effects were measured for saturated hydraulic conductivity and pore size distribution.
Evaluation of CMIP6 models for water and energy fluxes and analysis of source of errors over the Tibetan Plateau
Authors: Liu Su , Liu Zhu , Duan Qingyun
Published: February 2023

Climate change affects the hydrological cycle and induces extreme weather events, such as storms, floods and droughts. Adaptation to climate change needs to be based on assessments of future impacts. The new generation of Coupled Model Inter-comparison Project Phase 6 (CMIP6) is widely used in future flood prediction and drought risk assessment. However, many studies have found that CMIP6 global climate models for simulating land surface water and energy fluxes have significant biases, which poses a problem for using CMIP6 as input data for hydrological impact studies. Therefore, the output of CMIP6 cannot be directly used in hydrological models to project the impacts of future climate change. To overcome this problem, the correction of model output towards observations for its subsequent application in climate change impact studies has now become a standard procedure. And hydrological simulations generally use bias corrected output. But bias correction methods cannot really correct bias. The commonly used bias correction approaches only force the model outputs to match observations, and does not consider the mechanisms within the model and the interaction between variables. This study systematically evaluates water and energy fluxes of CMIP6 model over the Tibetan Plateau. Results show that the inter-model variability is substantial in temperature simulations. Snow that the largest component of the cryosphere responds significantly to changes in temperature. In the study, we study snow depth simulations corresponding to temperature simulations of different models over the Tibetan Plateau. Based on the water balance formula, analysis of how water balance fluxes respond to temperature changes in CMIP6, and determine the sources of error and ultimately lead to improved predictions.
Advantages and promises of deep neural operators for the prediction of wave propagation
Authors: Lehmann Fanny , Gatti Filippo , Bertin Michaël , Clouteau Didier
Published: February 2023

Physics-based deep learning experienced a major breakthrough a few years ago with the advent of neural operators. Beyond the traditional use of deep neural networks to predict the solution to a fixed Partial Differential Equation (PDE), these novel methods are able to learn the operator solution to a class of PDEs.Comparisons and analyses of popular neural operators such as Fourier Neural Operator and DeepONet have been conducted for numerical case studies. However, they are still lacking for more realistic problems in complex settings.In this study, we compare several neural operators to predict the propagation of seismic waves in heterogeneous media. Our database is composed of more than 12 million ground motion timeseries generated from 50,000 media. We quantify the accuracy of the neural operators, their memory requirements, and their dependence towards both the initial condition and the PDE parameters. We also propose insights on their possible extension to 3 dimensions.
Comparison of data-driven methods for linking extreme precipitation events to large-scale drivers: A case study from Copenhagen, Denmark
Authors: Antoniadou Nafsika , Sørup Hjalte Jomo Danielsen , Pedersen Jonas Wied , Bülow Gregersen Ida , Schmith Torben , Arnbjerg-Nielsen Karsten
Published: February 2023

Extreme precipitation events can lead to severe negative consequences on society, the economy, and the environment. To mitigate related risks, it is crucial to understand their natural causes. There is a vast number of methods in the literature analyzing their connection to large-scale drivers. Recently there has been much interest in using machine learning (ML) methods instead of traditional statistical models like regression. ML methods are based on algorithms adapting and learning from data. By contrast, regression models are based on theory and assumptions and benefit from domain knowledge for model specification. Because of its adaptability, ML is claimed to offer superior predictive performance than traditional statistical modeling and better manage a greater number of potential predictors. A few studies in climate research have compared the performance between these two approaches, but their conclusions are inconsistent, and some have limitations. We used five predictor variables - Geopotential height at 500hPA, Convective available energy (CAPE), Total column water (TCW), Sea Surface Temperature (SST), and Surface Temperature (SAT) using ERA5, the latest reanalysis dataset from ECMWF, and data produced by the Danish Meteorological Institute. All the predictors were not used directly as inputs but were preprocessed before modeling. We trained models using logistic regression (LR) and three commonly used supervised machine learning algorithms - random forests (RF), neural networks (NNET), and support vector machines (SVM) to predict whether an extreme event occurred over Copenhagen. In the LR framework, the predictor variables were modeled using restricted cubic splines to address potential nonlinearity. The training data are highly unbalanced, so using a traditional performance metric such as accuracy (ACC) could be misleading. In light of this, we use performance metrics specialized for unbalanced datasets: the ROC (receiver operating characteristic) curve as the primary measure and the area under the precision-recall curve, the Brier score, and ACC together with the true positive rate and the false positive rate at the optimal threshold as secondary measures.During the variable selection process, it was found that SST has the weakest relationship with extreme events, and its inclusion did not increase the model performance. Furthermore, the results showed that the LR performs similarly to more complex ML algorithms. SVM had the worst performance in all cases. While most of the top-ranked impacting predictors were nearly comparable amongst models, especially CAPE and TCW, we found discrepancies; SAT contributed to RF and NNET but not to LR.