bluuebunny/crossref_metadata_2025 huggingface dataset:
Relationship of the predictability of North Pacific Mode and ENSO with predictability of PDO
Authors: Dixit Jivesh , AchutaRao Krishna M.
Published: March 2024

PDO and ENSO are most prominent variability modes in the Pacific Ocean at decadal and interannual timescales respectively. Mutual independence between ENSO and PDO is questionable (Chen & Wallace, 2016). Linear combination of the first two orthogonal modes of SST variability in our Study Region (SR; 70oN - 20oS, 110oE - 90oW) i.e. mode 1 (interannual mode, we call it, IAM; ENSO like variability) and mode 2 (North Pacific Mode (NPM; Deser & Blackmon (1995)); a decadal mode) produces a PDO like variability (Chen & Wallace, 2016). It suggests that PDO is not independently hosted in the Pacific Ocean and can be represented by two linearly independent variability modes.To produce credible and skillful climate information at multi-year to decadal timescales, Decadal Climate Prediction Project (DCPP), led by the Working Group on Subseasonal to Interdecadal Prediction (WGSIP), focuses on both the scientific and practical elements of forecasting climate by employing predictability research and retrospective analyses within the Coupled Model Intercomparison Project Phase 6 (CMIP6). Component A under DCPP experiments concentrates on hindcast experiments to examine the prediction skill of participating models with respect to actual observations.As linear combination of  IAM and NPM in SR produces PDO pattern and timescales efficiently, we compared the  ability of DCPP-A hindcasts to predict  IAM, NPM, and  PDO. In this analysis we use output from 9 models (a total of 128 ensemble members), initialised every year from 1960 to 2010. To produce the prediction skill estimates.At lead year 1 from initialisation, the prediction of NPM,  IAM and PDO is quite skillful as the models are initialised with observations. In subsequent years, skill of either IAM or NPM or both drop significantly and that leads to drop in skill of predicted PDO index. Both the deterministic estimates and probabilistic estimates of prediction skill for DCPP hindcast experiments suggest that the ability of hindcast experiments to predict NPM governs the prediction skill to predict PDO index.Keywords: PDO, ENSO, NPM, CMIP6, DCPP, hindcastReferencesChen, X., & Wallace, J. M. (2016). Orthogonal PDO and ENSO indices. Journal of Climate, 29(10), 3883–3892. https://doi.org/10.1175/jcli-d-15-0684.1Deser, C., & Blackmon, M. L. (1995). On the Relationship between Tropical and North Pacific Sea Surface Temperature Variations. Journal of Climate, 8(6), 1677–1680. https://doi.org/10.1175/1520-0442(1995)0082.0.CO;2
Hydrological Impacts of Groundwater Depth on Lakes in Northern China: Exploring the Mechanisms through Climate Change and Human Activities
Authors: Cui Yingjie , Long Di , Cui Yanhong
Published: March 2024

Abstract: The depth of groundwater is a critical factor that significantly influences the development and conservation of both surface water and groundwater in lakes located in Northern China, exemplified by Baiyangdian (BYL), the largest lake situated in the North China Plain. It forms a critical foundation for the ecological integrity of BYL by quantifying hydrological fluxes and investigating variations in surface water and groundwater across distinct groundwater depths. To address this inquiry, we established a distributed hydrological model for the basin, enabling the simulation of surface runoff (horizontally) and vertical processes such as evapotranspiration and infiltration. Findings for the period 1966-1980 reveal an overall shallow groundwater condition in the Baiyangdian plain area, with a multi-year average depth of approximately 4 meters. Precipitation recharge, lake evaporation, surface water inflow, surface water outflow, groundwater inflow, and groundwater outflow during this phase were quantified at 187 million m3, 288 million m3, 960 million m3, 683 million m3, 160 million m3, and 40 million m3, respectively. The predominance of horizontal flux (62%) signifies rapid lake water replenishment. Conversely, during the later period of 1981‒2018, groundwater depth in the plain area substantially increased, averaging 23.48 meters. Precipitation recharge, lake evaporation, surface water inflow, surface water outflow, and groundwater outflow were computed at 179 million m3, 227 million m3, 294 million m3, 118 million m3, and 127 million m3, respectively. The horizontal flux contribution diminished to 22%, while the vertical flux surged to 78%, indicating slower lake water renewal and heightened risks of water quality degradation. Climate change and human activities emerged as drivers of rising groundwater depth, subsequently weakening water cycle dynamics over BYL. In the future, the comprehensive recovery of groundwater facilitated by the South-to-North Water Diversion for lake and river replenishment will play a pivotal role in reinstating water cycle dynamics and enhancing ecological integrity. This study establishes a foundation for understanding the intricate interactions between lakes, rivers, and aquifers as groundwater depth evolves over time. It holds significance for water conservation and the preservation of BYL's water quantity and quality in the future.Key words: Baiyangdian Lake; distributed hydrological model; hydrological flux; Groundwater depth
Multiscale Entropy (MSE) and Self Organizing Map (SOM): two useful tools for the interpretation of seismic signals in the Campi Flegrei Caldera
Authors: Grimaldi Alberico , Scarpetta Silvia , Amoroso Ortensia , Convertito Vincenzo , Galluzzo Danilo , Messuti Giovanni , Napolitano Ferdinando , Gaudiosi Guido , Nardone Lucia , Capuano Paolo
Published: March 2024

The Campi Flegrei caldera, located in Southern Italy, is one of the most hazardous volcanoes in the world. Seismic activity in the caldera has increased in the last year due to a new unrest phase of the volcano. This has driven the attention of the scientific community and both local and national media. The Campi Flegrei area is densely populated, with a very low signal-to-noise ratio, making the detection of small earthquakes or the recognition of non-seismic events more challenging.This study introduces a novel approach to the detecting and clustering of seismic signals. Unlike conventional methods that primarily focus on the regularity of seismic patterns, our research pivots towards a complexity-centric perspective using the Multiscale Entropy (MSE) algorithm, in conjunction with the Self-Organized Map (SOM) algorithm for effective data clustering. This methodological shift allows for a more nuanced exploration of the intricate dynamics inherent in seismic activities.Seismic signals are not random or chaotic but rather complex structures that vary across different scales. By employing the MSE algorithm, we unravel these complex patterns, offering insights into the seismic behaviours that traditional methods may overlook. Our findings indicate a significant correlation between the complexity of seismic signals and key geophysical events related to the dynamic of the volcano, suggesting that complexity analysis could be a significant tool in seismic monitoring and prediction.The analysed dataset is a six-month long continuous signals recorded in period at the V0102 temporary seismic station installed by the INGV-Osservatorio Vesuviano close to the Pisciarelli area, located in the Campi Flegrei caldera. MSE has been applied to one-minute-long traces of signal and clusterized through the application of the SOM analysis, revealing hidden layers of complexity across multiple scales.Moreover, the study explores the potential of integrating MSE analysis with other seismic analysis techniques to enhance the accuracy of seismic interpretations. By combining complexity analysis with traditional approaches, we aim to develop a more deep and comprehensive understanding of seismic signals, potentially leading to an improvement of the seismic risk assessment.
How your aerosol implementation choices affect your model’s climate system response
Authors: Monteiro Estela , Mengis Nadine
Published: March 2024

Anthropogenic activities have disrupted the energy balance of the planet since preindustrial era through, among other drivers, the emission of various greenhouse gases and aerosols. The largest uncertainty to current climate forcing and future projections relates to the effect of aerosols. Their different impacts on the planet’s radiative balance, that is, with direct radiative and indirect cloud interaction forcing, need to be considered accurately in simple policy-informing climate models. Especially in the context of high ambition mitigation scenarios, variability in the future development of spatiotemporal aerosol forcing will have a relatively large impact on climate projections. Accordingly, an accurate inclusion of the relevant processes onto the modeling scheme, such as the spatiotemporal level of detail chosen when accounting for aerosol forcing in simple(r) climate models must be carefully considered.Here we explore the impact of different aerosols implementation schemes in an intermediate complexity Earth system model configuration with an energy moisture balance model (UVic ESCM, version 2.10). While the global mean forcing is the same for all scenarios, we vary spatial and temporal resolution of optical depth maps or implement aerosol forcing as direct radiative forcing to the Earth system. These schemes are applied to relevant ambitious mitigation scenarios aiming at temperature stabilization, which will become especially relevant in the upcoming CMIP exercises. Using a newly developed assessment framework, we will provide insights into the impacts of this model implementation choice onto future temperature development, the carbon cycle and heat uptake processes. Ultimately these insights aim to improve, constrain and design better scenario simulations that are both applicable and relevant to the scientific and decision-making communities.
Improved Representations of Water-Power System Interactions to Inform Clean Energy Transition for Mainland Southeast Asia
Authors: Liu Zhanwei , He Xiaogang
Published: March 2024

Power systems are inherently water dependent. Future energy development needs to jointly consider the interaction between power and water systems, especially the stream dynamics related to both water temperature and availability. Such connection is particularly pronounced in thermal and hydro power plants, where once-through cooling systems face disruptions from high intake water temperature and water scarcity, and hydropower generation is affected by drought conditions. Equally important is the impact of power system operations on water temperature and availability. For instance, warm water discharged from thermal power plants can increase water temperature, while cool water released from reservoir bottoms can cause temperature declines. Additionally, hydropower operations can alter water availability through changes in reservoir storage. Despite the importance of those factors, existing electricity capacity expansion models typically do not fully consider such feedbacks between stream dynamics and power system operations, as they lack the capability to accurately represent cascade reservoir operations and cooling processes for thermal power plants at sufficiently high spatial-temporal resolutions. To address this research gap, we develop an integrated model coupling a hydrological model (Community Water Model, CWatM), a stream temperature model (River Basin Model, RBM), and a novel electricity capacity expansion model (Pathways for Renewable Energy Planning coupling Short-term Hydropower OperaTion, PREP-SHOT, https://github.com/PREP-NexT/PREP-SHOT) to better represent the two-way feedback between stream dynamics and power system operations. The coupled model, applied in Mainland Southeast Asia featured by a diverse array of hydropower reservoirs and thermal power plants, supports informed, sustainable, and environmentally friendly planning for future energy development in the region.
Assessing the Performance of the Weather Research and Forecasting (WRF) Model in Simulating Atmospheric In-Cloud Icing Over Fagernesfjellet, Norway
Authors: Punde Pravin , Birkelund Yngve , Virk Muhammad , Han Xingbo
Published: March 2024

Atmospheric icing ensues when water droplets in the atmosphere freeze upon interacting with diverse objects, presenting substantial hazards to infrastructure and leading to disruptions in both road and air traffic. This study introduces a detailed analysis of in-cloud icing conducted specifically over Fagernesfjellet, Norway. Utilizing the Weather Research and Forecasting (WRF) model, ERA-5 data was employed for both initial and lateral boundary conditions. The simulation covers a three-month period from October 1, 2022, to December 31, 2022, with a grid spacing of 9,3,1 km.Acknowledging the substantial influence of local terrain on icing conditions, the analysis prioritizes the highest model resolution. The determination of the icing load involves the utilization of a Makkonen ice accretion model, and the resultant values, alongside surface parameters, undergo validation against field measurements taken at Fagernesfjellet, Norway. The representation of supercooled liquid water (SLW) in numerical weather prediction (NWP) models is crucial for precise atmospheric icing forecasts. Hence, we conduct a comprehensive evaluation of the Thompson scheme's performance in simulating liquid water content (LWC) and, consequently, the icing load, along with general weather parameters associated with icing.From our preliminary analysis, the WRF model showcases effectiveness in simulating in-cloud icing conditions. WRF adeptly reproduces crucial surface parameters such as temperature, pressure, relative humidity, wind speed, and direction. Nevertheless, there are discernible differences between the observed data and WRF results, particularly noticeable in the case of wind speed and direction.
Exploring the modern-day sedimentary record of glacial margins in central Chilean Patagonia 
Authors: Mejías Osorio Paulina , Le Heron Daniel , Wohlschlägl Ricarda , Davies Bethan , Grasemann Bernhard
Published: March 2024

Glacial forefields host a wide array of processes and landforms, which can vary significantly, even within today’s overarching context of rapid melting and recession correlated to anthropogenic climate forcing. Detailed studies of the geomorphology and sedimentology of glacial forefields provide insight regarding sediment transport, meltwater pathways, and the behavior of the ice itself. Patagonia’s glaciers have been inventoried, there is vast knowledge of paleoglacier extent, and remote sensing has focused mainly on calculating geometrical changes and velocities. By comparison, detailed sedimentological analyses are long overdue and landsystems models for the present-day state of these environments require updating. This work focuses on the sedimentary processes that are occurring at modern glacial margins, specifically at selected sites in the Northern Patagonian Icefield and the neighboring Monte San Lorenzo massif to the east. High resolution geomorphological maps generated with photogrammetric data from an uncrewed aerial vehicle are presented. These maps, complimented with sedimentological facies descriptions and stratigraphic logging seek to characterize landsystems for the margins of glaciers in and near the Northern Patagonian Icefield, thus working towards an accurate reading of the sedimentary record and a better understanding of current glacial processes.
Quartz chemico-structural characterization: a tool for sediment source tracing
Authors: Aupart Claire , Lerouge Catherine , Lach Philippe , Trichard Florian , Boulay Manon , Rizza Magali , Valla Pierre , Voinchet Pierre , Rixhon Gilles , Tissoux Hélène
Published: March 2024

Quartz is ubiquitous within continental crust and can virtually be found within all rock types (plutonic, metamorphic and sedimentary). During erosion, weathering and sedimentation processes, it has a very high preservation potential and is often used to trace sediments production and transport dynamics. The QUARTZ project (Multi-methods characterization of quartz for source-to-sink tracing in alluvial sediment and dosimetry approaches – French ANR) aims to use quartz as a tracer for sediment sourcing river dynamics by combining conventional characterization method such as Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS), Laser Induced Breakdown Spectroscopy (LIBS), and Cathodo-Luminescence (CL), with dosimetric methods, such as Electronic Spin Resonance (ESR) and Optically Stimulated Luminescence (OSL) which are more classically used for Quaternary sediment dating. To this end, one must define a robust quartz chemico-structural and dosimetric signature that can be compared from one sample to the other. We also aim to better understand ESR, OSL, and CL signals controlled by structural quartz defects. These can be either intrinsic (e.g. dislocations, missing/supplementary O or Si), or extrinsic (foreign atoms), each technique being more or less sensitive to these different defects.In this study, we apply this multi-method approach to the various bedrock lithologies of the Strengbach catchment (ca.40 km²) draining a low mountain range located in the easternmost France (Vosges). These are largely dominated by crystalline metamorphic and plutonic rocks, with secondary Triassic sandstones (Buntsandstein). Bedrock samples have been treated mechanically and chemically to extract quartz grains. These grains have been analyzed using ESR and OSL techniques and mounted on thick sections (100 µm) for CL, LA-ICP-MS, and LIBS analyses. 100-µm thick-sections were used to prevent quartz tearing apart under LA-ICP-MS laser beam. Quartz characterization was completed by the study of whole-rock bedrock thick sections, analyzed with CL, LA-ICP-MS and LIBS approaches.Preliminary results allow identifying specific quartz chemico-structural signatures not only depending on rock type (gneiss, granite, sandstone) but also on formation process (magmatic, recrystallized, metamorphic or sedimentary). Further comparison between quartz analysis in whole rock and in separated grains samples permit to assess the impact of the chemical treatment on the quartz signatures and to identify quartz populations likely to be more represented in the alluvial sediments produced by the different lithologies. Finally, ongoing analyses point out the complex contribution of trace elements to OSL, ESR and CL signals.
Near real-time water vapour monitoring with shipborne GNSS for numerical weather prediction
Authors: Bosser Pierre , Tulet Pierre
Published: March 2024

MAP-IO (Marion Dufresne Atmospheric Program Indian Ocean) is a research project that aims to collect long-term atmospheric, biological and marine observations to document the under-instrumented areas of the Indian and Southern Oceans. To achieve this, the French Research Vessel (R/V) Marion-Dufresne, based in Réunion Island in the Indian Ocean, has been equipped with around twenty autonomous instruments to take measurements along its route.Among the instruments deployed on this R/V, a GNSS antenna has been in operation since autumn 2020, providing unique and continuous measurements of atmospheric water vapour in this part of the world. The data were initially transmitted daily to shore for ultra-rapid (day +1) and rapid (day +3) routine analysis. The quality of the retrieved tropospheric delays and integrated water vapour contents was highlighted in a previous study. With more reliable transmission facilities, raw GNSS data are now transmitted hourly and can be analysed with a latency of less than 30 minutes.In this study, we provide an initial assessment of the near real-time (h+20min) processing performed as part of this project. This evaluation is based on GNSS raw data collected in August 2023 during a rotation of the R/V Marion-Dufresne in the French Austral Islands (Crozet, Kerguelen, Saint-Paul and Amsterdam Islands). Processing is performed every hour over a 24-hour window, using the raw hourly data transmitted by the R/V and the cumulative real-time ephemerides and clocks provided by JPL / GDGPS. Only the last hour of each analysis is then considered. Over this period, the estimated tropospheric delays are in good agreement with the rapid routine solution, with differences of less than 1 cm RMS. The comparison with the analysis and the 6h forecast of the Météo-France's global numerical weather prediction model Arpège highlight the benefits of these shipborne GNSS measurements for numerical weather prediction.The medium-term objective of this work is to establish an operational procedure for the assimilation of these near real-time GNSS tropopheric solutions into numerical weather prediction models.
20-year-long sea level changes along the world’s coastlines from satellite altimetry: a new data set of coastal virtual stations
Authors: Cazenave Anny , Leclercq Lancelot , Leger Fabien , Birol Florence , Nino Fernando , Passaro Marcello , Legeais Jean Francois
Published: March 2024

In the context of the ESA Climate Change Initiative (CCI) Coastal Sea Level project, a complete reprocessing (including retracking of the radar waveforms) of high resolution (20 Hz, i.e. 350 m) along-track altimetry data of the Jason-1, Jason-2 and Jason-3 missions since January 2002 was performed along the world coastal zones. Different versions have been provided so far. The latest release (SL_cci+ coastal altimeter sea level dataset, v2.3) is now available to users. It is an extension in time of the previous data set (v2.2) which covers the period January 2002 to June 2021. A new improved processing for the waveform retracking and computation of the coastal sea level anomalies was developed and a new editing procedure for the coastal sea level trend computation was implemented. This new data set shows spectacular reduction of the data noise compared to previous versions, both in terms of sea level anomaly time series and trends. As a consequence, compared to the previous versions we now obtain an important increase of the number of virtual coastal stations (i.e., the location of the first valid point along the satellite track, with about 1200 sites at an average distance from the coast of about 3 km, including more than 200 stations at less than 2 km from the coast). The coastal sea level anomalies and trends of the altimetry-based virtual stations have been validated with tide gauge data where possible. An example in the Mississippi Delta is presented.
Near-real time detection of conflict-related explosions or suspicious events using seismological data 
Authors: Goertz-Allmann Bettina , Dando Ben D.E. , Koehler Andreas , Brissaud Quentin , Schweitzer Johannes , Kværna Tormod
Published: March 2024

Apart from classical earthquake monitoring, seismological data can also be used to detect explosions in near-real-time on both regional and global scales. We demonstrate how seismic and infrasound data can provide more comprehensive and objective information about conflict-related explosions or suspicious events that might be the result of targeted attacks. We can identify the underwater explosions at the Nord Stream pipeline infrastructure in the Baltic Sea in September 2022. Cross-correlation analysis allowed us to identify sub-events several seconds apart which can be associate with specific locations along the pipelines. Furthermore, we detect a signal at the Finish seismic array in October 2023 which may be associated with the damage along the Balticconnector. The other example is from Ukraine, where we present the ability to automatically identify and locate ground explosions related to the Russia-Ukraine conflict with data from the Malin array (AKASG). Between February and November 2022, we observe more than 1,200 explosions from the Kyiv, Zhytomyr, and Chernihiv provinces. Both seismic and infrasound detections can be used to verify and improve accurate reporting of military attacks and help to provide an unprecedented view of an active conflict zone. We analyze events with a variety of seismo-acoustic signatures and significant differences in explosive yield. These can be associated with various types of military attacks, including artillery shelling, cruise missile attacks, airstrikes, or the destruction of the Kakhovka dam NE of Cherson.
Resolving shallow cumulus clouds: insights from high-resolution airborne measurements 
Authors: Bagheri Gholamhossein , Thiede Birte , Schlenczek Oliver , Nordsiek Freja , Bodenschatz Eberhard
Published: March 2024

During the EUREC4A field campaign over the Atlantic Ocean near Barbados, we flew the Max Planck CloudKite aboard the German research vessel Maria S. Merian. In addition to three-dimensional wind speed, temperature, and humidity data, the scientific payload aboard CloudKite captured about one million holograms and half a million particle-image-velocimetry images, primarily in shallow cumulus clouds. The collected data allow us to capture the droplet size distribution and turbulence features with unprecedented resolution, thanks to the fast acquisition rate of the instruments combined with the low true air speed of the tethered CloudKite aerostat. We found that the clouds exhibit extreme variations in droplet size distribution both near the edge and in the core. The cloud droplets also exhibit clusters and empty regions, especially near the cloud edge.
The feedback of greening on local hydrothermal conditions in Northern China
Authors: Zhang Yu , Feng Xiaoming , Zhou Chaowei , Zhao Ruibo , Leng Xuejing , Wang Yunqiang , Sun Chuanlian
Published: March 2024

Northern China has experienced a significant increase in vegetation cover over the past few decades. It lacks a comprehensive understanding of how greening impacts local hydrothermal conditions. To address this issue, in our study, the RegCM-CLM45 model was used to conduct a thorough assessment of the impacts of greening on temperature, vapor pressure deficit (VPD), precipitation, and soil moisture. The findings revealed that the local climatic effects of greening varied across different drought gradients based on the aridity index (AI). In drier regions with AI0.3, a greater proportion of energy was lost through evapotranspiration, attenuating warming. Additionally, greening enhanced precipitation and soil moisture in drier regions and moderated their decline in wetter regions. Significantly, our research emphasized the effectiveness of grassland expansion and conservation as prime strategies for ecological restoration, particularly in drylands, where they could effectively alleviate soil drought. Given the diverse responses of different land cover transformations to local hydrothermal conditions in drylands, there is an urgent need to address potential adverse effects arising from inappropriate ecological restoration strategies and to develop an optimal restoration framework for the future.
Correlations between microbial activity and soil texture in Hungarian soils
Authors: Szécsy Orsolya , Rékási Márk , Szabó Anita , Szűcs-Vásárhelyi Nóra
Published: March 2024

As part of a national survey, 129 soil samples were taken countrywide across Hungary, in order to be representative of both soil types and intensity of cultivation of the country. The bulk samples were taken from 20 points of the diagonals of each sampling parcel, from the 0-30 cm surface layer. Among other soil physical parameters, samples were classified into soil textures based on the Arany yarn number. Microbial measurements were selected so that they could give an overall look at the actual biological soil state. Our goal was to find out, whether we could detect correlations between soil texture, and clay content in particular with the measured microbial parameters regarding such a heterogeneous and large group of natural soil samples. Biological activity of the soil samples were evaluated applying three tests: fluorescein-diacetate (FDA) and sucrose (invertase) enzyme activity tests as well as substrate induced respiration (SIR) measurement. The FDA test is suitable for estimating the soil’s microbial activity. The hydrolysis of FDA is based on the process of several soil enzymes hydrolysing colourless fluorescein-diacetate added to the soil. Released coloured fluorescein can be measured by spectrophotometry. Determination of sucrose (invertase) enzyme activity is founded on quantitative measurement of reducer monosaccharides emerging from the hydrolysis of sucrose. This test provides information on the carbohydrate metabolism processes in the soil. The most important indicator of soil biological activity is the degree of soil respiration that can be measured through the quantitative analysis of the CO2 produced by the decomposition of organic matter. Substrate induced respiration (SIR) method is based on a so-called respiration answer given by the microbial biomass in the presence of an easily utilisable substrate (glucose) being in saturated concentration. Statistical analysis of the data was performed with the programme StatSoft Statistica (Version 12 and 13). The results showed that according to the Kruskal-Wallis tests, correlations could be detected between soil texture and all three microbial parameters. Microbial activity raised in accordance with increasing clay content. It could therefore be verified that although microbiological state and activity of the soil is affected by several environmental factors, FDA and sucrose activity as well as SIR in our samples all depend on the content of clay minerals of the soil, as these can produce favourable conditions for the accumulation of enzymes. This research was funded by TDR project (KEOP-6.3.0/2F/09-2009-0006).
Experimental comparison of dissolved organic matter and nutrients leached from beach wrack by sea- and rainwater: A nutritional boost for the sandy beach subterranean estuary
Authors: Waska Hannelore , Banko-Kubis Hanne
Published: March 2024

Subterranean estuaries underlying high-energy beaches are efficient turnover sites for dissolved organic matter (DOM) and nutrients from marine and terrestrial waters. In addition, leaching of beach wrack during tidal inundation and precipitation can contribute to DOM and nutrient loads. However, the combined impact of diverse environmental settings on the release of DOM and nutrients from beach wrack has so far not been studied, although e.g., salinity oscillations and temporary exposure to sunlight are common in high-energy beach subterranean estuaries. Here, we present the results of an extensive beach wrack leaching experiment taking beach wrack type, age, sunlight exposure, and leaching matrix into consideration. A combination of UVA irradiation, advanced wrack age, and leaching by low-salinity artificial rainwater resulted in high dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) releases of mmoles- to moles per kg dry weight in the macroalga Fucus sp. Furthermore, jellyfish wrack released millimoles of TDN in artificial seawater incubations. Ultra-high-resolution analyses of DOM revealed a prevalence of molecular formulae resembling biochemical building blocks such as sugars, amino acids, and vitamins, indicating that the released DOM could be of substantial nutritional value for the heterotrophic microbial communities on and near beach wrack. An interesting finding was the high abundance of aromatic and humic-like DOM released from macroalgal beach wrack, which may impact typically used marine and terrestrial source- and sink proxies. As such, beach wrack DOM and nutrients could further complicate biogeochemical distribution patterns in the subterranean estuary.
Variability of middle atmospheric polar ozone and its effects on Arctic mesospheric tides
Authors: Shi Guochun , Liu Hanli , Krochin Witali , Tsutsumi Masaki , Gulbrandsen Njål , Stober Gunter
Published: March 2024

We perform continuous ozone measurements above Ny-Ålesund, Svalbard (79◦ N, 12◦ E) with ground-based microwave radiometers GROMOS-C to explore the short-term and interannual variability of ozone in the polar middle atmosphere covering from 2015 to 2023. GROMOS-C measurements exhibit good agreement with MERRA-2 reanalysis and Aura-MLS satellite data. This work focuses on understanding the influence of highly altered dynamics of sudden stratospheric warming events on ozone variations in the Arctic middle atmosphere and investigating the potential role of ozone in connection to mesospheric tides reported for such events. We extract the mesospheric semi-diurnal tides (SDT) and diurnal tides (DT) from the zonal and meridional winds recorded by nearby meteor radars located at Svalbard (79◦N, 12◦E) and Tromsoe (69◦N,18.5◦E), and Sodankyla (67◦N, 26◦E) in the Arctic regions. Furthermore, these tidal observations are compared with simulations by the specified dynamics–whole atmosphere community climate model with ionosphere/thermosphere extension (SD-WACCM-X). Utilizing the middle atmospheric ozone observations by GROMOS-C and MLS, we investigate the impact of ozone variability attributed to sudden stratospheric warming events on tides in the Mesosphere and Lower Thermosphere (MLT). Our findings suggest a possible connection between alterations in middle atmospheric ozone and the underlying circulation, which subsequently influences tidal propagation up to the mesosphere. This indicates that, in conjunction with radiative forcing, these dynamics may play a significant role in driving mesospheric tides.

 

 
Microplastic occurrence in coastal waters and aquatic faunas of the Western Black Sea 
Authors: Pojar Iulian , Baboș Teodora , Dobre Oana , Dobre Constantin
Published: March 2024

The Danube River, an important pathway connecting the western, central, and eastern Europe, serves as a route for various pollutants that ultimately accumulate in the Danube Delta and the broader Black Sea basin. These historically pristine regions now face escalating pressures from extensive tourism, intense fishing activities, regional conflicts, and inadequate waste disposal across the whole Danube basin. The imbalance in sediment flow, mainly caused by numerous upstream hydropower plants, aside the abovementioned anthropogenic factors, poses a severe threat to the deltaic and coastal ecosystems.Among the emerging pollutants, litter stands out prominently, with anthropic macro-objects pervading natural environments. Microplastics (MPs) constitute a category of the litter, characterized by solid, petroleum-based items sized between 1 µm and 5 mm. These particles are either preformed within these dimensions or result from the fragmentation of larger plastic debris due to natural weathering. Known for their fast spreading across diverse environments, MPs possess a porous texture that might absorb substantial amounts of various pollutants, significantly impacting the entire food chain, particularly aquatic species.Given the limited data on MPs pollution in Black Sea coastal areas, the precise impact on the natural environment remains largely uncertain. Furthermore, the correlation between MPs pollution in aquatic environments and the concentration of MPs within mussels and fish has yet to be established. To address these knowledge gaps, our study investigated the abundance of MPs in the water surface layer (average concentration of 0.63 MPs/m³) and in two distinct species: (i) the mackerel - Alosa immaculata – a pelagic fish captured at the Saint Geoge branch mouth of the Danube Delta during inland migration for reproduction, with an average concentration of 11.8 MPs/individual, and (ii) Mytilus galloprovincialis – a filter feeder mollusc collected near the Constanța harbour – with an average concentration of 6.05 MPs/individual.Our findings indicate that fibers represent the predominant morphology of MPs found in both species, while fragments dominate the surface water layer. Notably, before the tourist season, the distribution of MPs types differs, suggesting potential influences from coastal natural factors such as wind, waves, and currents on the variety of MPs encountered. This research underscores the urgency of further investigations to comprehend the intricate dynamics of MPs pollution in these vital ecosystems and the cascading effects on marine life.
An Automated Conformalized Causal Learning System for Enhanced Mineral Prospectivity Mapping
Authors: Jaya Evelyn Jessica , Wang Xinbing , Zhou Chenghu , Ye Nanyang
Published: March 2024

Mineral Prospectivity Mapping (MPM) is a crucial process in mineral exploration, traditionally hampered by subjective interpretations and labor-intensive methods leading to unreliable outcomes. Predominantly focused on Independent and Identically Distributed (IID) scenarios, traditional research in MPM often struggles to generalize in Out of Distribution (OOD) scenarios, which are vital for accurate mineral exploration. Addressing these challenges, we introduce an innovative automated conformalized causal learning system for MPM. This system integrates a comprehensive data preprocessing pipeline that includes interpolation, feature filtering, data augmentation, and splitting, effectively managing diverse and imbalanced geological datasets. A central component of the system is Bayesian Optimization, autonomously selecting optimal machine learning models and hyperparameters to significantly enhance performance over non-automated methods. The system's most significant innovation is the incorporation of conformalized causal learning, exceptionally effective in handling OOD data scenarios. This methodology introduces an 'uncertainty region' in predictive models through conformal prediction, substantially reducing misclassification risks, while causal learning elucidates complex cause-and-effect relationships among geological features, essential for precise mineral deposit predictions. We evaluated the performance of our approach on six datasets, where the Area Under the Receiver Operating Characteristic (AUC ROC) of our automated optimized system surpassed the baseline method by an overall 17.84%, and the false positive rate (FPR) was reduced by an overall 84.31%. This development marks a significant advancement in MPM, enhancing accuracy and efficiency in mineral resource exploration and setting a new benchmark in the field. Released as an open-source platform, it offers the geological community a highly efficient, adaptable, and user-friendly tool, poised to revolutionize mineral prospectivity mapping in varied real-world scenarios.
Active European Warzone Impacts Raptor Migration
Authors: Russell Charlie , Franco Aldina , Atkinson Philip , Väli Ülo , Ashton-Butt Adham
Published: March 2024

Wildlife can act as an important indicator of environmental damage and flagship for environmental restoration efforts. Human conflicts can have impacts on wildlife, from direct mortality and environmental damage to the displacement of people, changing institutional dynamics and altered economies (Gaynor et al. 2016; Sutherland et al. 2020; Gallo-Cajiao et al. 2023). Extreme anthropogenic disturbances related to conflict may act as a barrier to migrating birds and increase the energetic costs of migration (Kikuchi et al. 2019). On the 24th February 2022 the Russian Federation invaded Ukraine, with targeted attacks on Kyiv and the eastern regions (UN Secretary-General 2022). By the 3rd March, when the first of 21 tagged Greater Spotted Eagles entered Ukraine on migration, the conflict had spread to most major cities, including parts of western Ukraine (UN Emergency Relief Coordinator 2022). Using GPS tracks and conflict data from ACLED (Clionadh et al. 2010; ACLED 2022) we quantify how behaviour of a long-distance migratory species was impacted during their migration through conflict areas in a quasi-experimental before-after control-impact design accounting for meteorological conditions. Migrating eagles were exposed to conflict events along their migration through Ukraine, and exhibited different behaviour to previous years, using less stopover sites and making large route deviations. This delayed their arrival to the breeding grounds and likely increased the energetic cost of migration with sub-lethal fitness effects. Our findings provide a rare window into how human conflicts affect animal behaviour and highlight the potential impacts of exposure to conflict events or other extreme anthropogenic disturbances on wildlife. This work has wider implications for assessing the impacts of armed conflicts on wildlife globally to better understand how conflicts affect the environment and implement future restoration efforts.
Machine Learning for Daily Streamflow Forecasting in the Rhine River Basin: Modeling and Predictive Insights
Authors: Sheikh Khozani Zohreh , Ionita Monica
Published: March 2024

Accurate prediction of streamflow is crucial for various purposes, such as flood control, dam design and operation, water supply systems, and hydropower generation. Estimating streamflow in a catchment presents challenges due to factors such as chaotic distribution, periodicity in streamflow patterns, and intricate/nonlinear relationships among catchment elements. The limitations of traditional models and the growing availability of time series data on flow rates and relevant weather and climate variables are leading to an increased use of Machine Learning-based models. Among these, neural networks have proven to be highly effective for making accurate predictions. In this research, three different types of Machine Learning (ML) algorithm, Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Random Forest (RF), were employed to forecast the daily streamflow of the Rhine River (Worms catchment). The predictive features at Worms gauging station (Rhine River) encompassed lagged values of streamflow (Qs) from the previous 1, 2, and 3 days, flow rate at the Maxau station (Rhine River) with a single lag-time (Qm-1), and daily precipitation (P). In this study, the data from 1 January 2013 to 31 August 2021 was employed for building models (training), and the data from 1 September 2021 to 31 October 2023 was used for model validation. The performance of the proposed models in predicting streamflow were investigated using some quantitative metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash-Sutcliffe efficiency (NSE), and Percent bias (PBIAS). The results showed that the Maxau flow rate (Qm-1) and daily streamflow with one day lag (Qs-1) are the most effective input variables for forecasting streamflow at Worms gaugin station. According to the NSE metric, all models have very good predictive power, but the RF algorithm outperformed the others.
A New Method of Estimating the Magnetopause Speed of Motion
Authors: Ghosh Mrittika , Šafránková Jana , Němeček Zdeněk
Published: March 2024

The magnetopause is the boundary where the solar wind and magnetosphere pressures balance each other. Once the upstream solar wind conditions change, the magnetopause moves to a new position and changes its shape accordingly. Previous studies usually calculate the speed of magnetopause motion from parameters of two crossings that were close in location and time. However, such events are relatively rare. A new hypothesis suggests application of the ion speed in the magnetopause layers to estimate the speed of magnetopause motion. As a boundary that plasma cannot pass through, the ion speed in the magnetopause should be related to the magnetopause speed. Our study aims to check whether this hypothesis is correct or not by using numerous magnetopause crossings recorded by the THEMIS mission. The magnetopause speed will be first calculated using the traditional method and compared with the results estimated using the new method.
Comparing L1 to near-Earth data for magnetosheath jet studies
Authors: Blüthner Georg , Temmer Manuela , Koller Florian
Published: March 2024

This study explores large-scale solar wind structures and differences in observations between measurement points at L1 and near Earth. Specifically, we focus on coronal mass ejections (CMEs) and stream interaction regions (SIR) together with their distinct substructures. Our study is based on existing CME/SIR lists of events defined by Koller et al. [2022] in OMNI data. For the given time ranges, we compare timing and appearance for the structures in the solar wind plasma and magnetic field parameters as probed by ACE/Wind, OMNI, and THEMIS. Our approach involves creating a database of identified structures, especially those observed by multiple spacecraft, facilitating statistical analysis. Matrix profiles will be employed to unveil recurring patterns and relationships among the substructures measured at different locations. This research will contribute to a more comprehensive understanding of solar wind dynamics and magnetospheric responses to the specific structures which is especially important for magnetosheath jets which are short-lived pressure enhancements.
Land-Climate Nexus: Unravelling Extremes with Attention Networks
Authors: subhadarsini suchismita , Kumar D. Nagesh , Rao S. Govindaraju
Published: March 2024

The intricate interplay between land use, climate dynamics, and other contributing factors significantly influences the occurrence of extreme events such as droughts, floods, and heatwaves. Modeling this complex system in a high-dimensional space poses a formidable challenge, given incomplete understanding and limited availability of data. This study explores the application of deep learning approaches, specifically leveraging transformer architectures, to capture long-range dependencies in spatiotemporal data. These mechanisms are then employed to encapsulate the complex interactions between land use, climate, and other factors influencing extreme events. The proposed approach incorporates attention mechanisms, enhancing interpretability by highlighting crucial spatial and temporal features essential for forecasting. To evaluate the effectiveness of this methodology, a case study was conducted on the Godavari River Basin in India. Utilizing vegetation indices as a representation of crop type and land use, alongside climate data spanning from 2000 to 2020, the results provide valuable insights into the driving factors behind land use change and climate extremes in the region. The study not only demonstrates predictive capabilities of the proposed approach but also offers insights into the intricate relationships within the land-atmosphere feedback system. The extracted information is useful for making informed decisions related to land management, climate adaptation, and disaster risk reduction.
Merging personal weather stations with real-time radar rainfall estimates at the catchment scale
Authors: Rombeek Nathalie , Hrachowitz Markus , Wüthrich Davide , Uijlenhoet Remko
Published: March 2024

Real-time flood forecasting and warning during extreme rainfall events remains challenging since accurate and real-time available data are critical. Nowcasting based on radar rainfall can be utilized for this, as it has a high spatial and temporal resolution (i.e. typically 1 km and 5 min). However, the quantitative precipitation estimates (QPE) from the radar, upon which radar rainfall nowcasting is based, often contains substantial uncertainty and bias. While the QPE are usually corrected with official rain-gauge networks, these networks are sparse, and not always available in (near) real-time.Instead, personal weather stations (PWS) can be used, as they have a much higher density and are available in real time. While PWS are prone to several sources of error, quality control algorithms can be used to improve their accuracy. Previous research already showed that merging quality controlled PWS with radar rainfall estimates reduces the underestimation for 1-hour accumulated rainfall at the pan-European scale. However, this has not yet been investigated at the catchment scale. This research aims to investigate the potential of merging PWS data with radar rainfall estimates for different catchments in the Netherlands, by considering multiple rainfall events starting from 2018. The goal is to quantify the performance in relation to rainfall type, quality control algorithms and catchment properties, validated against the climatological gauge-adjusted radar dataset from the KNMI. The insights obtained from this research have the potential to be utilized for real-time radar rainfall nowcasting and consequently flood forecasting.