bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of H2O on the crystal chemistry of bridgmanite in the deep lower mantle
Authors: Zhang Li
Published: March 2024

The lower mantle is dominated by bridgmanite. Bridgmanite is nearly dry in coexistence with a hydrous phase [1]. Under the deep lower mantle conditions, the alumina-rich niccolite-type silica phase (Nt-phase) with a formula of Si0.7Al 0.3O1.85Hx can contain up to 4.6 wt.% water via the Si4+ = Al3+ + H+ charge-coupled substitution. [2]. Further, the presence of H2O changes the crystal chemistry of bridgmanite in the deep lower mantle. In contrast to iron-depletion in dry bridgmanite under similar pressure-temperature conditions [3], we found that ferrous iron is stable in bridgmanite under hydrous conditions. With multigrain diffraction methods allowing separation of individual grains in a multiphase assemblage, in combination with chemical analysis on recovered samples, we have obtained effects of H2O on chemical composition and crystal chemistry of bridgmanite under deep lower mantle conditions [4]. Our results imply that the existence of a water reservoir might be a major origin of lateral heterogeneity in the deep lower mantle.
[1]        T. Ishii, E. Ohtani, and A. Shatskiy, Earth and Planetary Science Letters 583, 117441 (2022).
[2]        L. Liu, H. Yuan, Y. Yao, Z. Yang, F. A. Gorelli, N. Giordano, L. He, E. Ohtani, and L. Zhang, Geophysical Research Letters 49, e2021GL097178 (2022).
[3]        L. Zhang et al., Science 344, 877 (2014).
[4]        L. Zhang, H. Yuan, Y. Meng, and H.-K. Mao, Engineering 5, 441 (2019).
Comparing water-disposal and CO2-storage induced earthquakes
Authors: Langenbruch Cornelius , Shapiro Serge
Published: March 2024

To date, only several small-scale CO2 storage projects, injecting about 1 Mt of CO2 per year, exist worldwide. Induced seismicity has been recorded during operation. Overall, magnitudes of the seismic events are small (below M=3). Nevertheless, there is the concern that future large-scale projects will induce significantly larger magnitudes, like it is observed for basin-scale waste-water disposal. For instance, large-scale water disposal in Oklahoma and Kansas (USA) induced thousands of widely felt M3+ earthquakes with a maximum magnitude of M=5.8. We analyze seismicity and injection data from CO2-storage projects including Quest (CA), Decataur (USA), In-Sahla (Algeria), Otway (AUS) and Gorgon (AUS). We compute the normalized seismic response of the subsurface to injection of a unit volume of CO2 using the Seismogenic Index (SI) and compare it to water-disposal case studies. We find that the SI at CO2-storage sites is smaller compared to water-disposal cases. It indicates a lower seismic hazard per injected volume of CO2. We discuss physical processes that could explain our observations and show how earthquake magnitude probabilities can potentially be upscaled, considering CO2 storage volumes needed in the future.
Ba-Mg isotopes in high Ba-Sr granites: implications for mantle mass transfer and crustal growth
Authors: Zhu Renzhi , Fowler Mike , Bruand Emilie , Huang Fang , Storey Craig , Wang Xiaojun , Chen Lihui , Yin Jiyuan , Lai Shaocong
Published: March 2024

High Ba-Sr granites have continental Hf-Nd-Sr-O isotope characteristics but evidence for mantle derivation. Consequently, crustal evolution models that rely on Hf-O isotopes incorrectly exclude them from the juvenile budget. It is therefore critical to understand the “enriched” crustal signature and constrain its volumetric proportion. Plutons from the Caledonian-type area show a range of enrichment severity ideal for novel isotopic investigations. Mg isotopes (δ26Mg value of −0.30 to −0.25‰ in a “depleted” example vs. −0.42 to −0.23‰ in an enriched comparator) lie largely within the normal mantle range. Ba isotopes (δ138/134Ba values of -0.05 to +0.23‰ and -0.19 to +0.03‰ for depleted and enriched, respectively) overlap normal mantle values but extend to lighter values, consistent with the addition of 2-5% pelagic sediment. Elemental relationships suggest sediment melting as the admixture mechanism. First-order estimates from the Caledonian orogen and global geochemical data suggest that ca. 10% of granites have high Ba-Sr character, thus representing significant unrecognized crustal growth.
A spatial perspective on internal phosphorus cycling in morphologically complex eutrophic lakes: the importance of stratification
Authors: Jilbert Tom , Zhao Siqi , Vesterinen Jussi , Niemistö Juha
Published: March 2024

Many eutrophic lakes suffer from long term accumulation of legacy phosphorus (P) in sediments. Repeated cycling of P between sediments and water column leads to delayed recovery from eutrophication even after abatement of external loading. Moreover, in complex multi-basin lake systems, legacy P can be internally redistributed over time, leading to spatial heterogeneity in regeneration and burial of P and consequent impacts on water quality. Few studies have attempted to map such internal variability in individual lakes in the context of understanding long term recovery from eutrophication. Here we use a combination of sediment trap deployments through one full stratification cycle (May-October 2021), sediment core biogeochemical analyses, and mass balance calculations, to quantify P cycling in Lake Hiidenvesi, a dimictic lake with 5 sub-basins in southern Finland. We show that exchange of P between sediments and water column is more intense in shallow (approximately 0-10 m depth) non-stratified sub-basins, due to both sediment resuspension and diffusive fluxes across the sediment-water interface. In contrast, deeper stratified sub-basins serve as P sinks by promoting sedimentation in relatively quiescent conditions. Due to lateral exchange of water and suspended materials between sub-basins, P is shuttled towards long term burial in deeper, downstream sub-basins. Budget calculations show that net sediment P burial exceeds external loading on the whole-lake scale, indicating a long-term trend towards recovery from eutrophication. However, temporary retention and repeated recycling of legacy P in the shallower upstream sub-basins continues to impact negatively on water quality, despite external loading reductions. The results have implications for understanding the timescales of recovery and for targeting restoration actions aimed at modifying internal P cycling to improve water quality.
Cascade effects of Greece using historical data of natural hazards: An operational tool in education
Authors: Tsoutsos Michail-Christos , Vescoukis Vassilios
Published: March 2024

Climate change constitute one of the main challenges mankind has to come up against, where of crucial importance is the undertaking of initiatives collaboratively by the countries in order to counter the consequences of climate crisis. The deterioration of the environmental conditions due to the alteration of climatic conditions has increased the likelihood of occurrence of various natural hazards (e.g. floods, storms, landslides, drought events, wildfires) from 2000 to 2019 as specified by the United Nations Office for Disaster Risk Reduction (UNDRR), which reinforced the multi-hazard profile of the hazard-prone areas. Greece has been affected by several disastrous events as stated in multiple data sources of natural hazards, where wildfires, floods and earthquakes have induced detrimental effects. However, there is a plethora both of hazard interrelations and types of hazards interactions that can exacerbate the implications caused by natural hazards. On the other hand, education and communication of hazard cascades will contribute substantially to the reduction of disasters, therefore it is indispensable dissemination activities of multi-hazard approaches to be implemented. The purpose of this research is to propose a service that leverages historical geospatial data or spatially referenced data, based on the literature of domino/triggering effect, in order to promote and assist preparedness actions for disaster chains in the context of education. AcknowledgmentsAuthors acknowledge the financial support provided by the Research Committee of the National Technical University of Athens (N.T.U.A.), which awarded Michail-Christos Tsoutsos with a Doctoral Scholarship and, thus, enabled him to carry out the research required for this kind of study. Grant number: 65/219100.
The cooling solution
Authors: De Cian Enrica , Crimi Jacopo , Mazzone Antonella , Squarci Gaia
Published: March 2024

Born out of a 5-year-long scientific research by the University of Venice, The Cooling Solution is a photographic project which investigates how people from different socioeconomic backgrounds adapt to rising temperatures and humidity. The project has been developed as an indoor and outdoor exhibition, a catalog, and a website. It points out inequalities linked to access to energy and cooling technologies in India, Indonesia, Brazil and Italy reflecting on the way cooling strategies, architecture and social habits affect our relationship to the increasing heat across the world. The project starts from scientific data and blends them with personal stories and portraits to return an immersive and engaging experience for the visitors. The successful exhibition held in Venice in 2023 resulted in a wide dissemination of the research outputs outside of the sphere European research projects usually reach, involving a broad, diverse and international audience. 
Key for the development of the project was the involvement of different skilled professionals, which added a crucial element to the mix. The coordination of work was assigned to a communication agency specialized in academic content, who guided the photographer in developing her reportages, but also developed a narrative which could hold together the dozens of scientific papers produced during the ERC project with the photography. The involvement of a professional curatorship then allowed to plan, layout and choose a subset of 67 pictures among the hundreds shot by the photographer, creating at last a multilayered narrative where all elements - texts, scientific data, photography -  coexisted without prevailing on one another. While planning and implementing the exhibition, the human dimension has always remained prevalent, knowing that the possibility to resonate and link with people's thoughts and possibly behaviors can be mediated through personal emotions.
A millennial-scale tephra event-stratigraphic record of the South China Sea since the penultimate interglacial
Authors: Kong Deming
Published: March 2024

Large volcanic eruptions have significant impacts on climate and environmental changes. The deposition of tephra in marine sediments may serve as an eruption recorder, but it has not been extensively studied in the western Pacific. This study explored a millennial-scale tephra event-stratigraphy with multiple indicators in a sediment core collected from the eastern South China Sea (SCS) basin. The magnetic susceptibility (MS), Fe and Mn concentration determined by X-ray fluorescence (XRF), and identification of individual ash particles were used to identify tephra layers and reconstruct the history of volcanic activity. Nine visible volcaniclastic units (VVU) and two cryptotephra layers have been identified based on their distinct features, as manifested by high MS, Fe, and Mn concentrations, and single-peak grain size distribution. The VVUs and cryptotephra layers reveal elevated volcanic activities. Using the radiocarbon age model and oxygen isotope stratigraphy, these episodes could roughly correspond to the following periods: 1-11 ka, 16-17 ka, 27-31 ka, 41-42 ka, 45-46 ka, 49-50 ka, 77-80 ka, 90-91 ka, 97-99 ka, 116-126 ka, and 132-140 ka. The alkenone-derived SST has significant glacial cycles and good synchronicity with other SCS SST records, which could partially help build the preliminary age model. Despite possible age errors larger than 1 kyr, the discovery and timing of tephra layers provide a preliminary framework to further investigate the impact of historical volcanic eruptions on climate changes.
Application of deep learning and remote sensing satellite data to assess glacier retreat for the past three decades in Himachal 
Authors: Prakash Chander , Sharma Rajat
Published: March 2024

Conventional glacier and glacier lake mapping change analysis using satellite data involves techniques based on band combinations and image processing. The results from computer processing of satellite data are sometimes affected by segmentation and thresholding techniques, due to which water bodies, shadows, rock surfaces, and other features are classified as glaciers and glacier lakes. Artificial intelligence (AI) based Deep learning (DL) algorithms for image classification, object detection, segmentation, and feature extraction have proven to be very efficient and accurate in recent years. Fully convolutional neural (FCN) networks-based U-Net architecture, which involves a gradual integration of superficial visual characteristics and semantic information extracted from images to segment small objects effectively, is used in the present study to extract and map glaciers and assess the change in glacier mass for past three decades, i.e. from 1994 to 2021.
Deep Learning-based algorithms can automate the glacial mapping process with an identification accuracy of 95 percent and are less time-consuming. The glaciated area in Himachal Pradesh decreased at a rate of 67.84 km2 per annum from 4020.6 km2 in 1994 to 2198.5 km2 in 2021. It is observed that from 1994 to 2021, the glacier area decreased approximately by a percentage loss of 1.678 per annum, with decreasing decadal trend from 2.31% in 1994–2001 to 1.398 in 2011–2021.
GIS-based landscape scenicness estimation using machine learning for visual impact assessment of wind projects deployment in Europe
Authors: Chen Ruihong
Published: March 2024

Wind energy is an important renewable energy source for the energy transition, but the implementation of wind projects faces many challenges. One of the major concerns is the landscape impact resulting from the high visibility of wind turbines. To determine a suitable location for a future wind farm, the landscape impact of the proposed project needs to be assessed based on the landscape quality and visibility to nearby populations.
This study presents the first step of the landscape impact assessment, determining the landscape scenic quality using a Geographic Information System (GIS) approach. Firstly, various landscape metrics, including naturalness, human impact, remoteness, ruggedness, diversity, and abundance of land covers, are calculated for Great Britain, Switzerland, and Germany using open data sources. In contrast to previous studies, the calculation of the landscape metrics considers the entire viewshed (in other words, all surrounding points in the line of sight) instead of taking a single value at the specified location. Secondly, the existing crowd-sourced landscape scenic value dataset for Great Britain (ScenicOrNot) is used to identify the relationships between the perceived landscape aesthetic value and the above-mentioned landscape metrics using machine learning algorithms. Subsequently, the found relationships/patterns are validated using the available scenicness data for Germany, which comes from an empirically-based modelling approach. Finally, the validated method is propagated to the case of Switzerland to show the first estimation of landscape scenic value in high spatial resolution.
The results are expected to reveal the major drivers of human perception of landscape aesthetic value and to provide a scalable method for quantitative estimation of landscape scenic value in Europe using open data. This would provide further insight for estimating the landscape impact of wind turbines beyond pure visibility analysis and expedite the deployment of wind power in Europe.
Profiling of the radiation environment of Io torus using Juno's ASC electron measurements
Authors: Herceg Matija , Jørgensen John L. , Denver Troelz , Jørgensen Peter S. , Connerney John E. P. , Bolton Scott J.
Published: March 2024

Since its orbital insertion around Jupiter on July 4, 2016, the Juno spacecraft has been navigating in a highly elliptical orbit, offering unprecedented insights into the gas giant's magnetosphere. A pivotal aspect of this mission is the analysis of the interaction between Jupiter's magnetic field and the surrounding plasma environment, particularly concerning the Io flux tube. The µASC, serving as an attitude reference for Juno's Magnetic Field investigation, has played a crucial role in continuously monitoring electron fluxes. These observations reveal a consistent depletion of electron fluxes in proximity to the Io flux tube.
To date, Juno has completed 57 close approaches to Jupiter, known as perijoves, during which it has traversed the Io flux tube over 200 times. These encounters have provided a wealth of in-situ electron flux data, enabling a comprehensive profiling of the Io torus electron population.
The data value chain-Connecting the dots from data to impact 
Authors: Bharati Luna
Published: March 2024

Mountain regions are undergoing tremendous and rapid climate and environmental change.  Technological advancements, data acquisition and management methods have greatly improved our ability to observe and understand the Earth's hydrological, climatic systems. However, there are still  data-to-policy gaps in critical areas such as Disaster Risk Reduction (DRR), especially in vulnerable mountain environments. This presentation will explore the central role of data services from global to local scales in bridging the gap between observations and informed decision making in DRR and resilience research and planning in mountain and glacier environments. Both successful and unsuccessful case studies will be presented, evaluated and discussed.
Revalidation process for IMS waveform technologies stations
Authors: Molero Adriana
Published: March 2024

S. Bazarragchaa1, P.Martysevich1, A. Kramer1, P. Campus1, A. Molero2, Y.Starovoyt2
1 CTBTO, International Monitoring System Division, Engineering&Development section, Wagramerstrasse 5, 1220 Vienna, Austria;2 Addproject, C/Conde Duque 48, bajo izda, 28015 Madrid, Spain
The revalidation process of International Monitoring System (IMS) stations of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) is a fundamental step of the IMS sustainment to demonstrate the compliance of such stations with the minimum requirements established at the time of their certification (IMS requirements).
Revalidation should be undertaken if any change occurs at a station that significantly affects its system response, detection capability or data availability (configuration change). Upon completion of the configuration change (or recapitalization) works at the station, a revalidation report is submitted by the IMS Engineering and Development Section of the CTBTO for consideration and review by an appointed Certification Group.
The report addresses, among several technical aspects, noise study analysis tailored for different equipment settings; station calibration; data format settings and population of relevant databases.
Performance Analysis of Near Realtime Tropospheric Delay Estimation using Low-cost GNSS Receivers
Authors: Rozsa Szabolcs , Ambrus Bence , Turák Bence
Published: March 2024

Permanent GNSS stations are routinely used for the estimation of tropospheric delays and gradients and provide valuable atmospheric parameters for numerical weather modelling. The spatial resolution of existing geodetic GNSS networks is usually too low to retrieve the high spatial variability of atmospheric water vapour.
Densifying the existing geodetic GNSS networks with low-cost receivers may provide a good solution to improve the resolution. Recently, a GNSS based atmospheric remote sensing system has been established in the Pannonian basin using both geodetic grade and lower cost permanent GNSS stations (Septentrio PolarX5 receivers). Tropospheric delays and gradients are estimated on an hourly basis and the products are disseminated in the frame of the EUMETNET E-GVAP programme.
Furthermore a countrywide GNSS network was also established for agricultural applications using low-cost ublox F9P dual frequency multi-GNSS receiver. The available GNSS observations provide a good opportunity to compare the accuracy of the ZTD estimates obtained at stations equiped with geodetic grade and low-cost GNSS receivers.
A key question in the application of the low-cost receivers and antennas is the lack of the proper antenna phase center models for all the available GNSS systems. To avoid this problem, both low-cost antennas used for this study were calibrated using a relative calibration technique using an absolute calibrated geodetic antenna as reference.
The results show that the Septentrio PolarX5 receivers shows similar performance to the geodetic grade receivers in terms of the monthly RMS of the ZTD residuals compared to numerical weather models proving that low-cost GNSS instruments can be beneficial for the densification of existing GNSS networks used for meteorological purposes.
Development of a Consistent Cloud Detection Approach for Creating Geostationary Satellite Cloud Climate Data Records
Authors: Trepte Qing , Smith, Jr. William , Palikonda Rabi , Yost Christopher , Bedka Sarah , Scarino Ben
Published: March 2024

Geostationary satellites (GEOsats) provide continuous cloud and meteorological observations over a fixed portion of the Earth’s surface, allowing them to monitor the movement of storm systems and their diurnal variation. For climate studies, geostationary observations provide valuable insight into cloud formation and evolution and how they influence the diurnal cycle of Earth’s radiation budget.
 
A long and consistent cloud record can be a valuable resource for evaluating changes in cloud systems and properties across the globe. Integrating observations from the different geostationary instruments, however, poses challenges due to their distinct characteristics, such as different spectral channels and calibrations as well as varying spatial resolutions to list a few.  As a result, deriving consistent cloud properties from multiple sensors without introducing artificial discontinuities in a time series remains a complex and challenging task.
 
The operational versions of NASA’s real-time Satellite ClOud and Radiation Property retrieval System (SatCORPS) cloud mask algorithms have been applied to imager data from the global constellation of GEOsats since 2010 to support weather applications, field campaigns, numerical modeling, and studies of cloud processes. These approaches use as much spectral information as possible for each satellite imager (all-channel mask) to characterize clouds as accurately as possible, but this approach can lead to cross-platform inconsistencies due to the application of different algorithms.  A different strategy is being developed to create cloud climate data records (CDR’s) for NASA’s CERES (Clouds and the Earth’s Radiant Energy System) mission from a long record of GEOsats that only uses spectral channels common on every satellite. Thus, a 3-channel (0.6, 3.9, 11 mm) algorithm for daytime cloud detection, and a 2-channel (3.9 and 11 mm) algorithm for nighttime have been implemented and tested. Recent advances to the 3-channel processing framework include refined radiative transfer models, revised clear sky background reflectance, improved surface properties (skin temperature and emissivity), and other updated ancillary data inputs. It’s anticipated that these changes will lead to a cloud CDR with improved cross platform consistency and regional accuracies compared to previous efforts.
 
This paper describes our new cloud detection approach and presents results of initial cross-platform consistency tests and evaluations with independent data from active sensors and from GEO analyses that utilize more spectral information. Remaining challenges and future work will be discussed at the conference.
Statistical associations of basin streamflow on sea surface salinity variability across major global deltas.
Authors: Khadim Fahad Khan , Getirana Augusto , Bindlish Rajat , Kumar Sujay
Published: March 2024

Sea surface salinity ( ) is a key parameter for the thermohaline circulation of global oceans, as well as the global hydrologic cycle. Near the deltas, inland streamflow through large catchments plays a crucial role in mediating salinity, which is vital for maintaining an agro-hydrological balance in the deltas. With recent remote sensing data sources providing both   and streamflow at global scales, we calculated the statistical associations of   with simulated basin streamflow ( ) at a monthly scale in 48 major deltas across the globe. The monthly   data was downloaded globally at   intervals from the SMAP RSS L3 products. The hourly streamflow data was extracted from HYMAP streamflow routing simulations (available at   spatial grids) within NASA’s LIS modeling framework. The streamflow data was spatiotemporally aggregated before performing the statistical analyses. We calculated the associations over different monthly-lags and plume distances and obtained the optimal correlations. The optimal correlation coefficients ( ) reveal strong anticorrelation phenomenon between   and   (  for seasonal data at 28 deltas, and   for de-seasoned data at 21 deltas). In addition to basin streamflow, we considered a number of sea surface climate forcings (precipitation, sea surface temperature, and wind speed) to perform similar statistical comparisons with  . The results revealed that   near the deltas are more influenced by basin streamflow in general. From a physical science perspective, we found consistent outcomes in the majority of deltas, with some irregularities in deltas with strong anthropogenic and management influences (e.g., deltas containing low forest cover and high reservoir areas). The anticorrelation phenomenon was more prominent in large deltas, specifically located near the tropical climates, which experience high streamflow and no ice-melting. We also found that the anticorrelations are more profound in river dominated deltas (e.g., deltas where the fluvial dominance ratio is greater than 1). The findings of this research will be useful for delta researchers aiming to devise global scale strategies amidst the rising threats of salinity intrusion.
Geospatial Analytics for Enhanced Supply Chain Sustainability: Introducing the SCo2-API Framework
Authors: Ouellette William , Surti Jinal
Published: March 2024

This session delves into the evolving landscape of climate regulations and voluntary commitments facing supply chains, with a specific focus on land-based supply chains such as agriculture and forestry. Recognizing the pivotal role of the first mile in contributing over 50% of greenhouse gas emissions and environmental externalities, the discussion centers on addressing the challenges posed by scope 3 emissions and the distributed responsibility inherent in the supply chain.Epoch introduces the SCo2-API, a comprehensive web application leveraging Google Earth Engine and Google Cloud. This platform facilitates the management of geospatial assets within the supply chain, encompassing plot data, agricultural practices, supply shed, and payment details. The SCo2-API further provides API endpoints to derive on-demand, spatio-temporally relevant sustainability metrics.The presented metrics include deforestation monitoring for compliance with European Union Deforestation Regulation (EUDR), Land Use Change (LUC), and Land Management (non-LUC) emissions estimates. These metrics serve to identify intervention hotspots and monitor environmental co-benefits such as carbon removals, water use, and biodiversity resulting from landscape restoration interventions.Additionally, the SCo2-API incorporates advanced capabilities such as automated sampling design and minimum sampling density requirements for field data collection. These features are crucial for validating and enhancing confidence in the sustainability metrics generated. The framework also integrates payment capabilities to support Payments for Ecosystem Services (PES) schemes based on validated sustainability metrics.Algorithmically, the SCo2-API ensures near-real-time results through a suite of scientific workflows. These include time series change detection using the Continuous Change Detection and Classification (CCDC) algorithm (Zhu et al., 2014), machine learning models predicting above-ground biomass and canopy heights using GEDI and ICESat data, canopy height heterogeneity as a proxy for landscape diversity (Rocchini et al., 2018), and evapotranspiration modeling as a proxy for water use (Melton et al., 2022). All scientific workflows are based on open datasets and satellite collections, aligning with open-source principles to ensure reproducibility and auditability of generated figures.This session aims to explore the scientific and technological dimensions of the SCo2-API framework, providing insights into its applications for advancing supply chain sustainability and meeting regulatory and voluntary commitments.
Future changes in the influence of the NAO on Mediterranean winter precipitation extremes in the EC-Earth3 Large Ensemble
Authors: Rivosecchi Andrea , Bollasina Massimo , Colfescu Ioana
Published: March 2024

One of the largest uncertainties in future climate projections is the interplay between internally generated and externally forced changes. This study investigates the changes in the link between the North Atlantic Oscillation (NAO) and Mediterranean winter extreme rainfall (monthly R95p and Rx1day) and consecutive dry days (CDD) by the end of the 21st century compared to present day. We compare two different future pathways and estimate the extent to which the NAO imprint is affected by the global warming level using the latest EC-Earth3 large ensemble historical and future experiments. The NAO is found to exert a similar control on European climate variability, regardless of the amount of warming. The expected range of winter extremes changes due to internal and unpredictable fluctuations of the NAO largely overcomes the signal associated with externally forced NAO variations. For most of the Mediterranean region, magnitude and even sign of projected changes in the NAO-congruent precipitation indices vary substantially across the individual ensemble members according to the corresponding evolution of the NAO. Internal variability provides an average basin-wide contribution of up to 90% or more to the total NAO-driven variability in SSP1-1.9, and of about 80% in SSP5-8.5. Sub-regionally, the anthropogenic component of the NAO link is more evident over the Iberian Peninsula and parts of the central Mediterranean. This emphasises the role of internal variability and related uncertainty in determining the future impact of the NAO via the large spread in the circulation responses. However, the NAO is found to exert a weaker influence on the extreme precipitation indices total variability in both future scenarios given their future marked increase in total intensity and variance as opposed to the negligible NAO-related trends. Opposite conclusions are drawn for CDD, which are projected to decrease in the future, especially in the northern Mediterranean. Thus, this study also highlights how the variability of future extreme precipitation intensity in the Mediterranean basin will be less dependent on the principal mode of internal climate variability, posing further challenges for prediction and adaptation to weather-related hazards.
Climate and hydrology control apparent rates of peat accumulation across Europe
Authors: Swindles Graeme T. , Mullan Donal J. , Brannigan Neil T. , Sim Thomas G. , Gallego-Sala Angela , Blaauw Maarten , Lamentowicz Mariusz Lamentowicz , Green Sophie M. , Roland Thomas P. , Fewster Richard ,   European peatland research group
Published: March 2024

Peat accumulates when there is a positive mass balance between plant productivity inputs and litter/peat decomposition losses. Here we examine apparent peat accumulation rates (aPAR) during the last two millennia from 28 well-dated European peatlands and find them to range between 0.005 and 0.448 cm yr-1 (mean = 0.118 cm yr-1). Our work provides important context for the commonplace assertion that peatlands accumulate at ~1mm per year. We find that relationships between aPAR and climatic variables are generally weak – summer temperature is the only significant climatic control on aPAR across our European sites. aPAR tends to be higher when water-table depth (reconstructed from testate-amoeba subfossils) is within 5-10 cm of the peatland surface. When a Generalized Additive Model and Gaussian Response Curve are fitted to the data, both methods show that the optimal water-table depth for highest aPAR is ~10 cm.  aPAR is generally lower when water table depths are 25 cm, which may relate to a decrease in plant productivity and increased decomposition losses, respectively. These findings corroborate contemporary experimental studies which examined the relationship between peatland water-table depth, or the thickness of the aerobic surface layer (the ‘acrotelm’), and the rate of peat formation. Our work suggests that for European peat bogs, an average water-table depth of ~10 cm is optimal to enable rapid peat growth and therefore carbon sequestration in the long term. This has important implications for peatland restoration and rewetting strategies, in our global efforts to mitigate climate change.
Analysis of UAV-based multi-spectral data over southwest Greenland for machine learning applications 
Authors: Tedesco Marco , Antwerpen Rafael , Fields Samuel , Ginoux Hugo , Huffman Braden , Prabhu Sushant , Samant Devan
Published: March 2024

The recent acceleration of mass loss from the Greenland ice sheet (GrIS) is a main driver of sea level rise and it has been projected to further accelerate in the coming years. Recent studies have highlighted the contribution of atmospheric processes (e.g., blocking) on enhanced surface mass loss as well as the evolution of surface properties associated with the presence of mineralogical and biological impurities that favor the enhanced absorption of solar radiation and promote more melting. 
Recent developments of machine learning (ML) tools are shedding new light on the processes driving the observed mass loss that are, otherwise, not possible  to study with more “traditional” tools. This is due to the skills of ML to explore the multidimensional space of the input data and discover patterns or relationships. However, ML tools should not be used as black boxes and the data fed to them should be treated in the same critical way as the models themselves. 
With this in mind, in the summer of 2023, we carried out the collection of UAV-based multi-spectral and multi-altitude data collection near the margin of the southwestern portion of the GrIS with the goal of understanding how such data can be used to train ML models and improve our understanding of the surface hydrological system over Greenland and address some of the following questions: how is the height of data collected influencing the information content within Uav data that can be used for ML tools ? What are the spatial scales driving information content and how can this knowledge be used to optimize data collection and learn more about the hydrological processes over Greenland ? What is the optimal number of wavelengths to perform unsupervised classification of the hydrological system and how can this information be properly scaled to satellite scales? 
In this presentation, we report initial results concerning the analysis of entropy loss – connected to information content – of the UAV multi-spectral data at different heights as well as results obtained from unsupervised computer vision methods to classify ice sheet surface features - ice, snow, water,  mineralogy and biology - from its spectral properties. We discuss also the results of a k-means clustering and of a deep learning model W-net, the variation in pixel classification is minimal. 
Lexcube for Jupyter: Interactive Earth System Data Cube Visualization in Jupyter Notebooks
Authors: Söchting Maximilian , Mahecha Miguel D. , Montero Loaiza David , Scheuermann Gerik
Published: March 2024

Data streams representing the Earth system both through modeling and remote sensing approaches, encompass a diverse range and massive amount of information. Unveiling insights at global and local scales becomes increasingly challenging for the wider public and the broader scientific audience as the temporal and spatial resolutions of data sets continually improve. An effective solution to this involves the development of fully interactive visualizations capable of rendering terabytes of data in real-time, spanning time, space, variables, and model variants. Lexcube.org, the Leipzig Explorer of Earth Data Cubes, was the first tool that allowed to explore and interact with large Earth system data sets in the form of an interactive data cube visualization in the web browser, but was limited to a few preset data sets.Here we present Lexcube for Jupyter, a Jupyter notebook extension building on top of the existing Lexcube.org software components, that allows to visualize any spatiotemporal or otherwise three-dimensional data as an interactive 3D data cube. The data cube visualization treats all three dimensions equally and, e.g., in the case of a spatiotemporal data cube, allows to inspect temporal patterns in a novel way. Interaction with the data cube is designed to be intuitive, also allowing touch gestures on touch-capable devices. Building on top of the powerful open-source libraries Xarray and Numpy, Lexcube for Juypter integrates effortlessly into the existing ecosystem of open-source data cube software components as it is able to visualize any gridded data set from those libraries, including remotely stored and chunked data sets. Furthermore, Lexcube for Jupyter allows to export the currently visible data cube as a new Xarray or Numpy object, allowing scientists to use Lexcube in their workflow for data selection and curation. In addition, new disciplines such as the atmospheric sciences may profit from Lexcube for Juypter as they can now visualize their own three-dimensional data that is not necessarily spatiotemporal, e.g., three-dimensional atmospheric humidity data cubes (latitude×longitude×pressure level) as seen on lexcube.org. Lexcube for Jupyter is open-source and available on GitHub and PyPi since January 2024.
Assessing Wind Energy Potential in Africa: A Comprehensive Analysis of Feasibility and Implementation Probability using GIS Data
Authors: Haeckner Benedikt , Zink Christoph , Pfennig Maximilian , Geiger David
Published: March 2024

Africa is facing a steadily increasing electricity consumption due to population growth, economic expansion, and rising living standards. To meet this demand in a sustainable and cost-effective manner, renewable energy sources, particularly wind power, should be harnessed. The African continent holds immense wind resources, making it necessary to quantify and evaluate this potential in terms of its quality and suitability. This study aims to conduct a comprehensive and spatially detailed analysis of the wind energy potential in Africa. Relevant site criteria for wind energy utilization, such as technical restrictions and considerations for human and environmental factors, have been quantified or considered using geospatial data from OpenStreetMap (OSM) and various freely available Earth Observation data. This data was processed with a high resolution of 50m x 50m. Recognizing that the probability of implementing a wind energy project is strongly correlated not only with the available wind resource but also with the likelihood of negative impacts on humans and the environment, a novel classification system called the Implementation Probability Index (IP-Index) has been introduced. The IP-Index enables the identification and assessment of potential risks associated with wind energy use at each location. By combining the consideration of technical restrictions with the IP Index, an initial quantification and evaluation of the technical potential can be achieved. To optimize project costs, priority is given to utilizing potential areas near existing infrastructure, especially those close to high-voltage power lines and cities. This merging of technical potential with additional criteria regarding available infrastructure results in realistically implementable potential areas near infrastructure for each country across Africa.
Analysis of emission characteristics of urban areas in East Asia based on the relationship between NO2 and CO2 derived from satellite data
Authors: Kim Jaemin , Lee Yungon
Published: March 2024

East Asia emits large amounts of greenhouse gases and air pollutants due to anthropogenic activities for socioeconomic development. The relationship between greenhouse gas and air pollutant concentration varies by region, depending on various factors, such as types of fuel and combustion and burning efficiency. This study investigated the relationship between Carbon dioxide (CO2) and Nitrogen dioxide (NO2) in several metropolitan cities in East Asia using measurements from the Geostationary Environment Monitoring Spectrometer (GEMS), Tropospheric Monitoring Instrument (TROPOMI), and Orbiting Carbon Observatory-3 (OCO-3). The regional differences in the NO2-CO2 relationship were discussed based on the regional anthropogenic activity pattern and various socioeconomic indicators. The NO2-CO2 relationship analysis can help understand the anthropogenic emission characteristics of each region. This study highlights the value of using recent satellite data in analyzing the relationship between air pollutants and greenhouse gases.
Assessment of the potential of different alkaline minerals to mitigate ocean acidification and sequester CO2
Authors: Rønning Jakob , Löscher Carolin
Published: March 2024

Ocean alkalinity enhancement is a novel and upcoming approach to removing CO2 from the atmosphere while mitigating ocean acidification simultaneously. We employed different concentrations of a silicate mineral, olivine (Mg2SiO4), as well as of several calcium-based minerals, including dolomite (CaMg(CO3)2), calcite (CaCO3), chalk (CaCO3), and limestone (CaO) on artificial and North Sea seawater over an experimental runtime of 98 days to monitor their impact on seawater carbon chemistry and their potential to counteract ocean acidification. We systematically analyzed the major and trace metals of the minerals. Olivine contained the highest concentrations of the potentially ecotoxic trace metals nickel (Ni) and cobalt. Dolomite and olivine showed both the highest concentrations of iron, which might be beneficial for phytoplankton communities in the ocean, as those are often iron-limited. Dissolution efficiencies of the different minerals varied and reflected the general challenge of achieving a useful proportion of dissolved minerals for ocean alkalinity enhancement (OAE). Our results showed further a comparatively high potential of olivine and dolomite to increase and stabilize seawater pH, while the Ca2+ based minerals regularly lead to opposite effects, reducing the pH. Alkalinity could be increased by olivine, albeit comparatively less by dolomite and the other minerals, confirming olivine as an ideal candidate for OAE. The promising outcomes for olivine and dolomite warrant further applicability assessments, especially considering the Ni sensitivity of local natural communities; dolomite emerges as a suitable alternative for more sensitive environments in the context of mineral-based OAE.
A Multiscale Evaluation of Soil Consolidation Concerning Land Subsidence and Integrated Mechanism Analysis in Typical Reclamation Areas
Authors: Yu Qingbo , Wang Qing , Chen Jianping , Wang Fengyan , Chen Huie , Wang Boxin , Yao Meng
Published: March 2024

Land reclamation has been increasingly employed in many coastal cities to resolve issues associated with land scarcity and natural hazards. Especially, land subsidence is a non-negligible environmental geological problem in reclamation areas, which is essentially caused by soil consolidation. However, spatial-scale evaluation on the average degree of consolidation (ADC) of soil layers and the effects of soil consolidation on land subsidence have rarely been reported. This study aims to carry out the integrated analysis on soil consolidation and subsidence mechanism in Chongming East Shoal (CES) reclamation area, Shanghai, at spatial-, macro-, and micro-scale so that appropriate guides can be provided to resist the potential environmental hazards. The interferometric synthetic aperture radar (InSAR) technique was utilized to retrieve the settlement curves of the selected onshore (Ra) and offshore (Rb) areas. Then, the hyperbolic (HP) model and three-point modified exponential (TME) model were combined applied to predict the ultimate settlement and to determine the range of ADC rather than a single pattern. With two boreholes Ba and Bb set within Ra and Rb, conventional tests, MIP test, and SEM test were conducted on the collected undisturbed soil to clarify the geological features of exposed soil layers and the micro-scale pore and structure characteristics of representative compression layer. The preliminary results showed that the ADC in Rb (93.1–94.1%) was considerably higher than that in Ra (60.8–78.7%); the clay layer was distinguished as the representative compression layer; on micro-scale, the poor permeability conditions contributed to the low consolidation efficiency and slight subsidence in Rb, although there was more compression space. During urbanization, the offshore area may suffer from potential subsidence when it is subjected to an increasing ground load, which requires special attention.
Flooding in Central Chile due to two extreme rain events: Insights from satellite remote sensors
Authors: Olivares Lester , González Gabriel
Published: March 2024

During the winter of 2023 in the Southern Hemisphere, Central Chile experienced two extreme rain events, impacting an area where over 70% of the country's population resides. The initial rain event transpired in late June, accumulating over 700 mm within five days. This led to significant damage to critical infrastructure, isolating towns and villages, particularly in the Mataquito basin. Subsequently, a second rain event in August affected the southern part of central Chile, specifically the Maule basin, mirroring the impacts observed in Mataquito. Given the infrastructure affectation and the need to mitigate risks it is imperative to identify flood-affected areas. This task is complicated considering the lack of in-situ measurements, but of increasing relevance considering the heightened likelihood of more intense rain events attributable to climate warming. Here, we employed a suite of satellite remote sensors, utilizing Google Earth Engine for analysis. Our approach integrated active and passive sensors, employing change detection algorithms such as Spectral Euclidean Distance (SED) and Spectral Angle Measurement (SAM) on Sentinel-2 images. Additionally, we employed supervised classification, utilizing the random forest algorithm to train the model for flood detection. To enhance accuracy, we calibrated our Sentinel-1 GRD results to the flood-identified areas in Sentinel-2 imagery. Our findings revealed a notable correlation between the identified flooded areas and in-situ measurements. Surprisingly, SAM demonstrated high accuracy in identifying flooding areas with elevated values, whereas SED presented a broader range of results that were often inconclusive, however, their use is limited by cloud presence. Despite these limitations, the calibration of active remote sensors using detected floods as ground truth proved valuable in establishing change thresholds for future rain events in similar regions.
 
 