bluuebunny/crossref_metadata_2025 huggingface dataset:
Connecting Variations in Vegetation and Stream Water Geochemistry along a Deglaciation Transect in western Greenland.
Authors: Ezukanma Izuchukwu , Black Megan , Flint Madison , McDaniel Stuart , Martin Ellen , Martin Jonathan
Published: March 2024

Ice retreat following the last glacial maximum, and the associated changes in landscape, climate, and hydrology, altered  nutrient fluxes off the landscape and fostered the development of new biological communities. The role of feedbacks between the new plant communities and geological processes, such as mineral weathering, are not well understood. Differences in water chemistry between catchments in southwestern Greenland that were deglaciated ~ 7 and 10 kya suggests that nutrient differences cannot be explained solely by abiotic factors alone.  One hypothesis to explain the variation in water chemistry is that plant communities mobilize different nutrients, either by releasing them directly or by differential weathering of the underlying substrates. To test this hypothesis, we first surveyed plant communities using a combination of Floristic Habitat Sampling and modified Point Quarter method in forty-five 1.25 m2 plots across five watersheds in 2022. The watersheds were ~ 150 km apart with one near the ice edge exposed ~6.8 ky ago and the other near the coastexposed ~ 10 ky. ago Using NMDS and ANOSIM (α = 0.05) analyses, we found that species compositions across locations were distinct (stress score = 0.15; p = 1e -04, R = 04025). Next we compared the vegetation distribution data  to elemental concentrations of dissolved nutrients (Li, Mg, Al, P, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Si) from adjacent streams using a Mantel test (method = Spearman, permutation = 9999). The variation in vegetation is correlated with the stream elemental composition data (p = 0.0043). The correlation between the two matrices suggests that either plant community composition drives variation in nutrient concentrations, or plant community and nutrient fluxes may both respond to another environmental variable. To distinguish between these alternatives, we are currently conducting mesocosm weathering experiments to test whether tundra plants vary in their ability to promote biogenic weathering, and whether biogenic weathering is sufficiently enhanced to explain variations in nutrient elements dissolved in streams and vegetation in southwestern Greenland.
Unlocking precision in flash flood forecasting: A synergistic approach using machine learning and satellite-ground integration.
Authors: Muñoz Paul , Muñoz David F. , Orellana-Alvear Johanna , Célleri Rolando
Published: March 2024

This research introduces an innovative approach to flash flood forecasting in poorly monitored or precipitation ungauged catchments. Leveraging data from satellite-based (PERSIANN-CCS) and ground-based precipitation estimates, our methodology is anchored in the Random Forest (RF) algorithm. We propose a hydrological model that integrates process-based Feature Engineering (FE) strategies, including a comprehensive connected component analysis (CCA) applied to satellite imagery, a soil moisture proxy, and subflow division. These strategies collectively aim to enhance the precision of short-term forecasting. We proved the utility of the proposed model in a 300-km2 catchment representative of the tropical Andes of Ecuador. While acknowledging potential reservations about the quality of hourly satellite data through an evaluation of the PERSIANN-CCS database at a microcatchment-wide scale, the proposed hydrological model achieved satisfactory forecasting models. We demonstrated forecasting performances for lead times up to three times thecatchment’s concentration time (Nash-Sutcliffe efficiency varying from 0.95 to 0.61). The success of our forecasting model is attributed to the synergistic fusion of ground- and satellite-based precipitation, coupled with FE strategies that integrate crucial physical knowledge into the models. Notably, the CCA enriches the input feature space of the RF models by deriving key precipitation attributes from precipitation objects otherwise hidden in timeseries. We assert the potential to exploit freely-available satellite information as an advantageous opportunity for the RF algorithm and other Machine Learning (ML) techniques. This refined approach not only facilitates efficient forecasting of peak flows w but also holds consequential implications for operational applications, including Flood Early Warning Systems (FEWSs), and contributes valuable insights into understanding precipitation-runoff responses in catchments constrained by sparse monitoring networks. Furthermore, the study unveils variations in the hyperparameterization of RF models across lead times, emphasizing the importance of incorporating physical knowledge into the modeling process. The inclusion of such insights, coupled with a meticulous approach to hyperparameterization, addresses persistent skepticism surrounding black-box, data-driven models. By highlighting ML hydrological models as a metaphorical "blank page," we advocate for their role as a testing ground where hydrological forecasting hypotheses can be scrutinized atop statistical and computational advantages.
What drives variability of the Australian summer monsoon?
Authors: Heidemann Hanna , Brown Josephine , Narsey Sugata
Published: March 2024

The variability of northern Australian rainfall is related to local processes and remote teleconnections, which operate on subseasonal to interdecadal timescales. This includes the Madden-Julian Oscillation, Indian Ocean Dipole, El Niño-Southern Oscillation (ENSO) and Interdecadal Pacific Oscillation. The influence of these climate drivers and local sea surface temperatures (SSTs) on northern Australian rainfall evolves during the wet season, from austral spring through to autumn. Our study shows that ENSO as well as SSTs in the Timor Sea, Arafura Seas and Coral Sea are the key sources of rainfall variability in the pre-monsoonal months September to November. SST indices explained up to 50% of variance in observed northern Australian rainfall in October and November between 1940 and 2023. However, the teleconnection between northern Australian rainfall and ENSO, and also the influence of local SSTs, breaks down with the onset of the Australian summer monsoon in late December. This leads to 0% explained variance in northwestern Australian rainfall and 9% explained variance in northeastern Australian rainfall in January using SST indices only. This presentation will discuss which processes and feedbacks might instead drive rainfall variability over northern Australia during the monsoon season and how they differ from pre- and post-monsoonal conditions.
Carbon-cycle modelling and aspects of Phanerozoic climate change
Authors: Torsvik Trond H. , Royer Dana , Marcilly Chloe , Werner Stephanie
Published: March 2024

Greenhouse gases trap heat in the Earth’s atmosphere and warm our planet and on geological time-scales CO2 is the most influential greenhouse gas in modulating atmospheric temperature. During most of the Phanerozoic (past 540 million years), our planet was warmer than today, and a greenhouse-dominated climate (80%) was only interrupted by three periods of cold glacial conditions during the end-Ordovician (Hirnantian) glaciation, the Permo-Carboniferous (~330-260 Ma) and the second half of the Cenozoic (34-0 Ma). Icehouses are characterized by lower CO2 concentrations and temperatures, and a modern CO2 threshold for continental-scale glacial inception is estimated to 500 ppm. But with a fainter sun, the glacial inception threshold during the Hirnantian (445 Ma) glaciation was probably closer to 1000 ppm.
CO2 concentrations cannot be measured in deep time, and we therefore must rely on proxies, or models. For the past 450 million years, CO2 proxies during greenhouse climates average ~1100 ppm whilst the Phanerozoic icehouse intervals average ~480 ppm. But a proxy-based picture of CO2 concentrations before 450 Ma is lacking and thus CO2 levels for most of Earth’s history must be estimated from carbon-cycle models. Models are also important for capturing the processes (sources and sinks) that can explain shifting greenhouse and icehouse climates and can loosely be classified as inverse or forward models, depending on whether isotopic proxy data are parametrized or predicted from the model, respectively. Both model types, however, incorporate several biological and geological/tectonic forcing parameters that should be similar in all models.
Carbon-cycle models predict very different atmospheric CO2 levels for most of the Phanerozoic, differing by more than 4000 ppm and model-proxy differences can exceed 5000 ppm. Many of the relatively large, modelled differences in atmospheric CO2 are arguable caused by differences in time-dependent parametrization of plate tectonic degassing and silicate weathering, and benchmarking of carbon-cycle models are urgently required. In this contribution we focus on carbon-cycle modelling with GEOCARB_NET — a user-friendly version of the GEOCARB model. In GEOCARB_NET input parameters can easily be changed, tested, and compared with other models (e.g., COPSE, SCION and GEOCLIM). The system also contains databases for CO2 proxies and temperatures that can be visualized together with CO2 predictions. We highlight how key input parameters can seriously affect reconstructed CO2 levels but also how models and proxies can better be reconciled.
Mantle Transition Zone (MTZ) beneath the contiguous US revealed by ambient noise cross-correlations
Authors: Aiman Yongki Andita , Lu Yang , Bokelmann Götz
Published: March 2024

Understanding the nature of the Mantle Transition Zone (MTZ) can foster our knowledge about the dynamics of the Earth, especially related to the vertical heat and mass exchange between the upper and the lower mantle. The MTZ, characterized by seismic-velocity discontinuities at depths of 410 km and 660 km, is conventionally studied using seismic waves emitted by earthquakes. However, this approach suffers from a typically uneven distribution of earthquakes, biases in earthquake location, and the complexity of earthquake processes.In this study, we used body waves retrieved from ambient noise correlations to map the mantle transition zone beneath the US. We analyzed cross-correlation functions from more than 3500 seismic stations, including the EarthScope USArray stations during its deployment time frame between 2004 and 2013. We obtained clear short period (
Optimizing Maximum Height Inferences through Neural Networks for the Spanish Tsunami Early Warning System
Authors: Rodríguez Gálvez Juan Francisco , Macías Sánchez Jorge , Gaite Beatriz , Castro Díaz Manuel Jesús , Cantavella Juan Vicente , Puertas Luis Carlos
Published: March 2024

Tsunami Early Warning Systems (TEWS) play a crucial role in minimizing the impact of tsunamis on coastal communities globally. In the NEAM region (North-East Atlantic, the Mediterranean, and connected Seas), historical approaches involve using Decision Matrices and precomputed databases due to the short time between tsunami generation and coastal impact. Overcoming real-time simulation challenges, the EDANYA group at the University of Málaga developed Tsunami-HySEA, a GPU code enabling Faster Than Real Time (FTRT) tsunami simulations. This code is successfully implemented and tested in TEWS of countries like Spain, Italy, and Chile, this code has undergone rigorous verification and validation processes.In collaboration with the National Geographic Institute of Spain, we have extended the work previously done where we take advantage of the machine learning techniques and proposed a first approach to the use of neural networks (NN) to predict the maximum wave height and arrival time of tsunamis in the context of TEWS with very good results. This approach offers the advantage of minimal inference time and can be executed on any computer. It accommodates uncertain input data, delivering results within seconds.As tsunamis are rare events, numerical simulations using the Tsunami-HySEA are used to train the NN model. This phase demands numerous simulations, necessitating substantial High-Performance Computing (HPC) resources. Approximately 300,000 simulations have been done to cover different faults in the Atlantic Ocean.The goal is to develop neural network models for predicting the maximum wave height of such tsunamis at multiple coastal locations simultaneously.  To cover Huelva and Cádiz coast, 78 points in the coastline have been selected for their predictions. The main importance of this work is that the models developed will be implemented in the Spanish TEWS which will produce an estimation of the tsunami impact in seconds. AcknowledgementsThis project has received funding from the European High-Performance Computing Joint Undertaking (JU) through the projects eFlows4HPC (No 955558) and ChEESE-2P (No 101093038) and by the EU project DT-GEO (No: 101058129).
Spanish Network for Supercomputing (RES) grants AECT-2022-1-002, AECT-2022-3-0015 and AECT-2023-1-0028.
Habit Classification of PHIPS Stereo-Microscopic Ice Crystal Images
Authors: Nehlert Franziska , Grulich Lucas , Schnaiter Martin , Spichtinger Peter , Weigel Ralf , Järvinen Emma
Published: March 2024

Clouds play a major role in the global radiative budget. Microphysical properties, such as the shape (or habit), of individual ice crystals define their optical properties and consequently affect the cloud radiative effect. Therefore, a better understanding of ice crystal morphology is crucial in improving climate modelling.
With the Particle Habit Imaging and Polar Scattering (PHIPS) instrument, a unique airborne measurement probe that simultaneously captures optical and microphysical properties of single ice crystals, we collected a large dataset of high resolution in-situ microscopic images of natural ice crystals. In addition to the outstanding image quality and corresponding visible level of detail, one of the key features of the PHIPS is that it simultaneously takes two images of each individual ice crystal under different viewing angles, resulting in stereo images.
Over 100,000 stereo images from multiple field campaigns have been manually classified using a tree-based classification system with 22 leaf nodes. Due to increasing instrument performance and frequent application in field projects an ever-increasing data amount makes manual classification of future PHIPS images impractical, and has motivated us to develop an automated classification scheme.
To automate classifying our images, we apply deep learning methods to our data and use convolutional neural networks (CNNs). The previously manually classified data is split into a training, validation and test dataset.
A description of our classification system of ice crystal morphology will be given. In addition, performance results for different CNN architectures will be presented. 
Flood Impact on Urban Mobility: An Assessment of Subway Infrastructure Vulnerability in China
Authors: Gao Xuyan
Published: March 2024

In recent years, the increased frequency and severity of extreme weather events, especially flooding, have drawn urgent attention to the associated risks and consequences. Flooding is a major concern due to its potential to cause significant damage to infrastructure, property, and public health. The impact of flooding on transportation and safety can be severe, as it may obstruct roads, disrupt daily travel and commuting, and even cause internal power outages and service suspensions in subway systems, posing a significant challenge to urban transportation.
This study focuses on analyzing the potential damage to subway stations in China using flood hazard maps with different return periods. To identify the core stations or transit hubs with high influence, we utilized the subway station network to determine the top 10% of stations with the highest eigenvector centrality. We then divided the urban population based on their proximity to the nearest subway stations, using the population distribution map. This allowed us to measure the distribution of passenger volume within the urban area's subway system and floodplain.
To assess the vulnerability of the core stations, we calculated the transportation facility damage percentage with flood hazard map depth. This helped us identify the most vulnerable core stations, as well as those with the highest influence. Finally, we estimated the total value lost in the flooding area by calculating the damage value of China's transportation facilities.
These results allowed us to assess standards for subway flood protection, ensuring that the impact on urban subway infrastructure during flood events is minimized, thereby facilitating people's travel and daily lives.
Global assessment of atmospheric and land surface drivers of heatwaves
Authors: Uckan Yigit , Ruiz-Vasquez Melissa , De Polt Kelley , Orth Rene
Published: March 2024

Heatwaves are extreme weather events characterized by exceptionally high temperatures that have severe impacts on society and ecosystems. Their magnitude and frequency are increasing with climate change in many regions. They are driven by both atmospheric and land surface processes such as advection or reduced evaporative cooling. The contributions of these individual drivers to the formation of heatwaves have been analyzed in case studies for major past events with model experiments. At the same time, the global relevance of heatwave drivers remains unclear.We perform a global analysis with reanalysis data to determine the relation of heatwave temperatures to (i) atmospheric variables such as wind, pressure, and pressure differences, each at different geopotential heights, as well as (ii) land surface variables such as evaporative fraction, enhanced vegetation index, and surface net radiation. First, we identify the hottest day in each grid cell during the period 2001-2020. We also determine the values of the driver variables on this day. Then, for each driver variable, we select five days from the entire study period where the variables’ value most closely matched the hottest day value (=analogues). Next, we compare the averaged temperature anomalies of these analogues to those of the hottest day. The more similar the analogue temperature anomalies are (=hotter), the more relevant the driver variable is deemed. This is done for the three hottest days in each grid cell, ensuring that they are at least 15 days apart from each other to belong to separate heatwave events. The results show that pressure at the 500 hPa level is the most relevant driver of heatwaves in the mid-latitudes, while in the tropics a combination of variables plays a more important role than individual variables. Radiation is the second most relevant driver in many regions, particularly in tropical areas. In most cases, several drivers seem to contribute to the heatwave events such that only their aggregated analogue temperature anomalies can match the observed anomalies. These findings confirm previous case studies which highlighted the relevance of atmospheric circulation patterns such as blocking as well as reduced evaporation related to vegetation water stress. For the first time we identify the relevance of these processes across the globe, and with observation-based data. This can contribute to a better management and potential mitigation of heatwave temperatures and their impacts.
Harnessing the power of forward models: past, present and future
Authors: Hogan Robin , Mason Shannon , Jakub Fabian , Fielding Mark
Published: March 2024

Variational retrievals, data assimilation, and model evaluation in observation space, all rely on accurate instrument simulators, or forward models. The scientific challenge is to find innovative approximations to the radiative transfer that make them fast enough to use iteratively, while retaining accuracy. In this presentation I will summarize how the development of various forward models, particularly in the context of synergistic retrievals from EarthCARE and the A-Train, has the capability to reveal important cloud and precipitation properties that would otherwise remain hidden, and potentially even to develop new satellite concepts. For example, our radar and lidar “Multiscatter” model enables the extinction profile to be retrieved in ice and liquid clouds even in the presence of lidar multiple scattering.  Our “FLOTSAM” solar radiance model can work with profiles containing arbitrary combinations of particles, and surprisingly can help improve rain-rate retrievals by better providing the additional information needed to partition the radar path-integrated attenuation into the contributions from liquid clouds and rain. The Two-Stream Source Function (TSSF) infrared and microwave radiance model enables us to interpret 94-GHz brightness temperature, which provides important additional information on precipitating ice and liquid clouds.  I will end by presenting a new radiance model for cloud/storm-resolving models that can efficiently represent horizontal radiation transport between columns; this could enable future retrievals and assimilation to take full account of 3D radiative effects.
Source identification and health risk assessment of atmospheric PM2.5 and PM10 - bound polycyclic aromatic hydrocarbons in Faisalabad, a future megacity in South Asia
Authors: Ibrahim Muhammad , Aslam Afifa , Mahmood Abid , Yousaf Balal
Published: March 2024

The current study defines the characterization of PAH in PM2.5 and PM10 samples collected from five different sectors (residential, health, commercial, industrial and vehicular zone) of Faisalabad, Pakistan. Regarding PAH (poly aromatic hydrocarbons) very little information is available which is a persistent environmental pollutant in Faisalabad, Pakistan. It was reviewed from previous studies that the concentration level of PAH was observed greater in during winter because of climatic effects and changes in the sources of emission. The sampling sites were the busiest and heavy-traffic areas of Faisalabad. PAH’s calculated through Gas Chromatography. PAH was found in the sequence of Naph (0.001) ˃ Phe (0.001) ˃ Fl (0.001) ˃ Ant (0.01) ˃ Ace (0.001) ˃ Acy (0.001) against chemical characterization of PM2.5 and PM10. The average concentration of PAH was found to be 110.97 and 115.24 μg/m3 for PM2.5 and 117.14 and 122.84 μg/m3 for PM10 in the summer and winter seasons, respectively. Among the determined polycyclic aromatic hydrocarbons Naph (0.001) was found to be in highest concentration.
The Future of Hot-Dry Events in the World’s Breadbasket Regions
Authors: Dietz Victoria , Baehr Johanna , Borchert Leonard
Published: March 2024

We use a 50-member large ensemble of the CMIP6 version of the MPI-ESM1.2-LR model to examine the future of hot-dry compound events at 1.5 and 2°C of global warming. By targeting the largest maize production areas (breadbasket regions) and their corresponding growing seasons, we tailor our analysis to food production, indicating potential future threats to global food security. Our results suggest a notable shift in the extremes associated with maize harvest failure in the breadbaskets between 1.5 and 2°C of global warming, highlighting the value of mitigating climate change and the future need to adapt to climate challenges in the agricultural sector.Our analysis shows a significant increase in the likelihood of these extremes during maize growing seasons across almost all examined regions and variables. In particular, the occurrence probability of heat events and hot-dry compounds at least doubles in most regions when the world warms from 1.5 to 2°C. Locally, cumulated heat excess increases everywhere, while the spatial extent of heat consistently expands across all regions in contrast to the relatively stable pattern we find for precipitation as we transition from one level of global warming to another. We additionally explore spatial compounding, where multiple breadbasket regions experience simultaneous extremes in the same growing season, exacerbating global food security challenges. Scenarios that were virtually impossible in the past, such as hot-dry events affecting at least three regions simultaneously, take on non-zero probabilities in a world that is 1.5 or 2°C warmer. The probabilities of simultaneous heat and hot-dry compounds in a 2°C warmer world significantly exceed those in a 1.5°C warmer world, to the extent that there is little to no overlap between the corresponding ensemble spreads.
Landslides detection and risk analysis of typical landslide along the Mekong river (Chamdo section) in the eastern Tibetan Plateau.
Authors: zhang Jiajia , Tian You , Chen Long , Li Xude
Published: March 2024

Abstract: The Mekong River is one of the major international rivers in the Hengduan mountainous region of the eastern Tibetan Plateau. Because of the canyon geomorphology, weak lithology, and strong human engineering activities, a large number of landslides have developed along the Mekong River (Chamdo section). The area also contains human settlements such as the urban area of Chamdo city and the county town of Chaya, as well as human engineering facilities such as G214 and the Sichuan-Tibet Railway under construction, so the detection and risk analysis of landslides are of great practical significance.
Based on the field investigation, combined with optical images and InSAR deformation interpretation, 75 landslides along the main stream of the Mekong River were detected, and the spatial distribution characteristics and main influencing factors of these landslides were further summarized. Chaya landslide complex is one of the typical landslides in the Mekong River, and Chaya County is its direct threat. Through the optical remote sensing images and InSAR deformation interpretation, combined with the UAV field investigation, it was found that SBAS-InSAR revealed that the areas of landslide clusters with deformation rates greater than 20 mm/y have been undergoing different degrees of collapse and slippage in recent years, and the secondary destabilization of the accumulation body in the areas with deformation rates up to 68 mm/y has almost caused the danger of blocking the river; The comparative analysis of rainfall and deformation rate during the period of 2018~2020 concluded that the deformation increment of the landslide cluster is strongly correlated with rainfall, but with a certain degree of hysteresis. Rainfall, river erosion and human engineering activities are the drivers of sustained deformation damage. Finally, the numerical simulation analysis of the disaster risk of the potential landslide areas was carried out by Massflow software, and the results showed that once the destabilized landslide occurs in some areas, there will be the risk of destroying the houses and blocking the river.
An Economy-Climate Model forQuantitatively Projecting the Impact ofFuture Climate Change and ItsApplication
Authors: Chou Jie Ming
Published: March 2024

Quantitatively projecting the impact of future climate change on the socio-economy and exploring its internal mechanism are of great practical signiﬁcance to adapt to climate change and prevent climate risks. Based on the economy-climate (C-D-C) model, this paper introduces a yield impact of climate change (YICC) model that can quantitatively project the climate change impact. The model is based on the YICC as its core concept and uses the impact ratio of climate change (IRCC) indicator to assess the response of the economic system to climate change over a long period of time. The YICC is deﬁned as the difference between the economic output under changing climate condition and that under assumed invariant climate condition. The IRCC not only reﬂects the sensitivity of economic output to climate change but also reveals the mechanism of the nonlinear interaction between climate change and non-climatic factors on the socio-economic system. Using the main grain-producing areas in China as a case study, we use the data of the ensemble average of 5 GCMs in CMIP6 to project the possible impact of climate change on grain production in the next 15–30 years under three future scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5). The results indicate that the long-term climate change in the future will have a restraining effect on production in North region and enhance production in South region. From 2021 to 2035, climate change will reduce production by 0.60–2.09% in North region, and increase production by 1.80–9.01% in South region under three future scenarios. From 2021 to 2050, compared with the climate change impact in 2021–2035, the negative impact of climate change on production in North region will weaken, and the positive impact on production in South region will enhance with the increase in emission concentration. Among them, climate change will reduce grain output in North region by 0.52–1.99%, and increase output in South region by 1.35–9.56% under the three future scenarios. The combination of economic results and climate change research is expected to provide scientiﬁc support for further revealing the economic mechanism of climate change impacts.
Scientific support on coastal vulnerability for a new urban master plan
Authors: Mannarini Gianandrea , Salinas Mario Leonardo , Verri Giorgia , Santos Da Costa Vladimir , Barzaghi Riccardo , Carrion Daniela
Published: March 2024

Coastal vulnerability to rising sea levels, impacting both coastal assets and ecosystems, poses a significant challenge for urban planners. Successful planning requires a deep understanding of both the natural and anthropic characteristics of a region, leading to resilient strategies capable of adapting to sea level rise. While scientific knowledge is crucial for informed decision-making, an operational interface between science and policy is not always available.In 2021, the Municipality of Lecce in the Apulia region of Italy, with a 20 km long low-lying sandy coast along the Adriatic Sea, initiated an update of its long-term urban plan [1]. The initial draft of the plan underwent a collaborative phase with the local community, involving seven public meetings held between December 2022 and March 2023. Following this phase, dedicated thematic boards were convened, bringing together specific stakeholders, technical experts, and political decision-makers. The CMCC Foundation provided advice on various aspects, particularly addressing coastal vulnerability to sea level rise.The consultancy unfolded through various stages, initiating with the Municipality's quest for information regarding future sea levels. Later on, they contributed a high-resolution digital terrain model of the territory. Leveraging this data, CMCC generated preliminary flood maps for two climate scenarios, utilizing tools such as Google Earth Pro street views [2] and Instagram Reels [3] to provide user-friendly visual representations.For the target year of 2060 outlined in the updated master plan, projections suggest that the mean sea level in Lecce could reach or surpass the current level of higher high waters. This poses a significant risk to houses constructed irregularly on the beach by removing the dunal belt, while saltwater intrusion threatens agricultural land [4]. Despite uncertainties in climate scenarios and coastal flooding models, maintaining a high level of collaboration between scientists and policymakers is crucial. This institutional partnership, along with a better instrumental monitoring of Lecce's coastal territory, can ensure aligning research developments with actual user requirements, including both citizens and decision-makers.References[1] PUG-Lecce, https://www.puglecce.it[2] Dataset of future shoreline of Lecce, https://zenodo.org/records/8009645[3] Reel of coastal flooding maps of Lecce, https://www.instagram.com/p/CzLN90buLOZ/ [4] Mannarini et al. 2024, https://doi.org/10.13140/RG.2.2.18379.57123 submitted to Frontiers on Climate - special issue “Coastal Climate Monitoring and Downscaling for Adaptation Planning in the Adriatic Sea”
Integration of Wind Resource Assessment through Multi-scale Coupling Method
Authors: Sun Shanxun , Li Hao
Published: March 2024

Assessing wind energy resources holds paramount significance in addressing the conventional energy crisis and ensuring the sustained, rapid development of the national economy. The study of wind resource assessment serves as a critical indicator used to evaluate the construction and operation of large-scale wind power projects, which holds crucial guiding significance in selecting wind farm models, equipment procurement, and operational revenue optimization. Therefore, aimed at enhancing the precision of wind resource assessment in complex terrains, this paper presents a method utilizing machine learning coupled with multi-scale numerical simulation. Firstly, wind profile simulation results are extracted from the mesoscale Weather Research and Forecasting (WRF) model. Subsequently, to integrate multi-scale temporal and spatial data, these mesoscale wind profiles are fitted using multi-task learning(MTL). Finally, the turbulence is derived by a periodical running precursor on Computational Fluid Dynamics (CFD), utilizing the fitted outcomes as input boundary conditions for microscale simulations, complex terrain is incorporated to enhance the accuracy of wind resource assessment. The results show that the proposed method can improve the assessment accuracy of wind resources of wind farms by mesoscale data, which is valuable to a certain extent in real-world applications.
Relative Alpha in the MHD with open magnetic field boundary and its application to the solar activity
Authors: Yang Shangbin , Buchner Joerg , Santos Jean , Skala Jan , Zhang Hongqi
Published: March 2024

An instability criterion in the MHD with the open boundary of magnetic field is proposed in this paper. We use a series of linear force-free extrapolation field, in which the normal part of magnetic field is fixed, to obtain the linear fitting coecient called relative alpha by using the co-joined value of magnetic free energy and magnetic flux at the open boundaryand the relative magnetic helicity We calculate this coecient of the magnetic field above active regions NOAA 8210 and NOAA 11429 obtained by the photospheric-data-driven  MHD  model. It is found that the fitting coecient is a good proxy of the criterion to indicate the occurrence of instability after which the magnetic reconnection happens and caused the fast release of magnetic energy. We also applied this method to the continuous evolution of three-dimension magnetic field of NOAA 11158 based on the measurement of photospheric vector magnetic field of SDO/HMI by the Non-linear Force-Free (NLFF) extrapolation method. The calculated coecient when the major f lare happened based on the extrapolation data is very close to the expected ones, which perfectly reflects the occurrence of instability and the dierence is even less than 7%. This relative alpha is very helpful to evaluate how far it is from the instability in the MHD and quantitatively estimate the occurrence of solar eruption in the space weather forecast.
Probabilistic inversion with HMCLab: solving the eikonal and wave propagation problems
Authors: Zunino Andrea , Aloisi Giacomo , Keating Scott , Fichtner Andreas
Published: March 2024

Probabilistic inversion as a tool for nonlinear inverse problems has been long advocated, but the practical difficulty of solving high-dimensional problems has been a major obstacle. Recent developments and innovative tools, however, are starting to allow us to tackle increasingly complex problems. In seismic tomography, for instance, despite impressive advances in scale and accuracy, uncertainty quantification is still frequently neglected or considered impractical.One approach for enabling probabilistic inversion in this type of problem is the combination of the Hamiltonian Monte Carlo (HMC) method with the use of adjoint methods to compute gradients, which makes sampling problems with tens of thousands model parameters tractable. This method uses the gradient of the objective functional to help drive the sampling process towards high-probability regions in the model space, allowing for more efficient solution of relatively large inverse problems. Prior information can also be accounted for in the algorithm, provided it is possible to efficiently compute its gradient.Here we present two examples from seismology: nonlinear traveltime inversion with an eikonal solver and full waveform acoustic tomography. Using the HMC algorithm and adjoint state gradients, we obtain an ensemble of models characterising the posterior distribution. Using such model ensembles, general uncertainty estimates can be made, as well as targeted uncertainty estimates addressing specific questions about the model parameters.The algorithms to solve the above-mentioned problems are implemented in the Julia language version of the open-source package HMCLab, a framework to solve different geophysical problems. More specifically, we employ finite difference-based solvers for both the eikonal and full waveform examples combined with the NUTS algorithm, a partially self-tuning variant of the classic HMC method.
Multi-spacecraft and multi-instrument investigation of the solar wind properties
Authors: Telloni Daniele
Published: March 2024

This talk reviews all recent results achieved in the investigation of solar wind properties using data from the Metis coronagraph aboard Solar Orbiter jointly with in-situ measurements acquired by Parker Solar Probe. Specifically, results on the propagation of the solar wind from the solar corona to the very inner heliosphere, the onset and evolution of turbulence, the link between the microphysical properties of the solar wind and the large-scale properties of the coronal source regions, the estimation of the rate of energy transfer by turbulence into the corona, and the energy budget in the corona will be presented. Finally, some possible future studies will be briefly discussed.
Controls of multi-stage strike-slip faults on migration and accumulation of hydrocarbon: Ordovician reservoirs in the north slope of Tazhong, Tarim Basin, China
Authors: Ouyang Siqi , Lyu Xiuxiang
Published: March 2024

The diversity of stress states in different regions along the principal displacement zone of strike-slip fault leads to the complexity of hydrocarbon migration in the fault zone, especially under multi-stage tectonic movements and hydrocarbon charge conditions. The pools with diﬀerent ﬂuid phases are distributed along strike-slip faults in the Tazhong area of the Tarim basin. However, the coupling and comparison of strike-slip faults, hydrocarbon migration, and distribution patterns remain poorly understood. In this research, we integrate structural analysis and geochemical methods to analyze the control mechanism of multiphase strike-slip motion on hydrocarbon migration pathways and their influence on hydrocarbon distribution. The strike-slip fault formed as a result of a three-stage evolution with different slip directions: the Middle to Late Ordovician (sinistral), Silurian to Devonian (sinistral), and Permian (dextral). The change in the occurrence of strike-slip faults and the combination of secondary and tertiary strike-slip faults lead to the formation of linking, bend, and tip damage zones. The regular changes in maturity and chromatographic geochemical parameters in the direction away from strike-slip faults indicate the overall pattern of hydrocarbon migration along strike-slip faults. However, it is not possible to explain the changes in the properties of adjacent regions. Therefore, we propose a model in which the characteristics of fault motion during the hydrocarbon migration period (crude oil: the Silurian-Devonian and Permian, gas: the Himalayan) and the combination of faults within the fault zone jointly control hydrocarbon migration and distribution. The reversal of the Late Caledonian and Late Hercynian faults from sinistral to dextral resulted in changes in the local tension area within the fault damage zone. Therefore, the migration pathways of the two phases of crude oil are different, and the maturity of crude oil around the fault is different. During the Himalayan period, the strike-slip fault and the intersection of the faults opened and controlled by NE-trending principal stress. The degree of tensioning of the fault has led to different natural gas charging intensities in the neighboring area. This research explains the mechanism of hydrocarbon distribution in the Tazhong area from the perspective of multi-stage strike-slip faults and provides a reference for hydrocarbon exploitation of complex strike-slip fault zones.
Fundamental-Harmonic pairs of type III radio emissions in interplanetary space
Authors: Jebaraj Immanuel Christopher , Krasnoselskikh Vladimir , Pulupa Marc , Magdalenic Jasmina , Bale Stuart
Published: March 2024

Type III radio bursts are not only the most intense but also the most frequently observed solar radio bursts. However, a number of their defining features remain poorly understood. Observational limitations, such as a lack of sufficient spectral and temporal resolution, have hindered a full comprehension of the emission process, especially in the hectokilometric wavelengths. Of particular difficulty is the ability to detect the harmonics of type III radio bursts. Here we report the first detailed observations of type III fundamental–harmonic pairs in the hectokilometric wavelengths, observed by the Parker Solar Probe. We present a statistical analysis of the spectral characteristics and polarization measurements of the fundamental–harmonic pairs. Additionally, we quantify various characteristics of the fundamental–harmonic pairs, such as the time delay and time profile asymmetry. Our report concludes that fundamental–harmonic pairs constitute a majority of all type III radio bursts observed during close encounters when the probe is in close proximity to the source region and propagation effects are less pronounced.
European high resolution soil quality products 
Authors: poggio Laura , Heiden Uta , Angelo Pablo , Karlshoefer Paul , Egmond van Fenny
Published: March 2024

High resolution reliable and quality controlled soil information products are fundamental for a range of applications related to climate change and sustainable management. Integration of remote sensing information is a key startegy to obtain more accurate and relevant products.  

This contribution provides an overview about input-products, covariates and soil products including image examples and methodologies (digital soil mapping, DSM).  DSM uses a statistical model to integrate products derived from Copernicus satellites Sentinel-1 and 2, environmental covariates such as digital elevation models, ERA-5 products and soil ground truth information from the LUCAS survey and other available sources. Several data products are derived from the Sentinel-2 time series using the Soil Composite Mapping Processor (SCMaP). It comprises mean reflectance composites as well as specific soil reflectance composites (SRC) that contain undisturbed and bare soils. Additionally, the bare soil frequency is a measure for the visibility of the bare soil within the whole observed time period in percent. EO based soil products were generated for primary soil properties (SOC, pH, texture), derived soil properties and some basic soil health indicators. The soil observations were split in 10 equally sized folds for cross-validation. Random Forest models were obtained with the ranger package, with the option to build Quantile Random Forests (QRF) to obtain pixel-based uncertainty.   

This contribution will discuss the advantages and pitfalls of using high resolution remote sensing products with a sparse point data sampling such as LUCAS.
Exact timing, sulfur spread and global climate footprint of the caldera-forming Mt. Mazama eruption, the largest volcanic eruption of the Holocene.
Authors: Sigl Michael , Van Dijk Evelien , Gabriel Imogen , Abbott Peter , Nicolussi Kurt , Pearson Charlotte , Salzer Matthew , Burke Andrea , Leahey Aidan , Sugden Patrick , Chellman Nathan , McConnell Joseph , Timmreck Claudia , Jungclaus Johann , Krüger Kirstin , Adolphi Florian , Riede Felix , Severi Mirko , Toohey Matthew
Published: March 2024

Large volcanic eruptions are key time markers in paleoclimatology because they inject large quantities of volcanic fallout (such as sulfuric acids and tephra) into the atmosphere which is then widely distributed and deposited in environmental archives such as ice cores, lakes and peat bogs. They also produce strong climate effects, imprinted in climate archives such as tree-rings. The caldera-forming eruption of Mount Mazama (Crater Lake, Oregon, USA) some 7700 years ago ranks among the largest eruptions of the Holocene but little is known about its exact timing and global-scale climate impacts. Here we use new high-resolution ice-core analyses of volatiles (S, Cl), particle-size distribution, crypto-tephra and sulfur isotopes (33S, 34S), from ice cores in Greenland and Antarctica, to constrain the date, stratospheric sulfur injection, global aerosol distribution and climate forcing of this eruption. We further demonstrate that the climatic effects left distinctive fingerprints in ultra-long tree-ring chronologies from North America and Europe allowing the date of this eruption to be pinned to a specific year, thereby aligning climate proxy records in North America, Greenland and Europe on a common timeline. Using an ensemble of fully-coupled Earth System Model simulations we identify some key regions experiencing large anomalies in temperature and hydro-climate following the Mt. Mazama eruption. These extreme conditions were not only relevant for hunter-gatherer communities and early agricultural societies emerging in Eurasia, that experienced these compounding effects, but they also help us in identifying a global existential risk arising from comparable eruptions in the future.
Investigation of Spatio-Temporal Variations of Ground and Water Temperatures Using UAV-Based Thermal Camera in the High Arctic Catchments, SW Spitsbergen
Authors: Alphonse Abhishek Bamby , Wawrzyniak Tomasz , Hanselmann Nicole , Osuch Marzena
Published: March 2024

The High Arctic region is experiencing rapid climate changes, with rising temperatures impacting one of the most fragile environments on the planet. This study employs Uncrewed Aerial Vehicle (UAV) technology equipped with Zenmuse H20T thermal camera to investigate the spatio-temporal variations of ground and water temperatures in the catchments of Southwest Spitsbergen. The UAV-based thermal imaging provides high-resolution and real-time data, allowing for a comprehensive understanding of temperature dynamics in this remote and challenging environment. The integration of UAV technology and in-situ measurements facilitates the collection of data at unprecedented spatial and temporal scales, allowing for a more detailed analysis of temperature trends and patterns.The study focuses on assessing ground temperature variations across different land cover types to discern the influence of seasonal variations on these components. Moreover, this study extends its scrutiny to the thermal patterns of Arctic hydrological systems, encompassing channels and ponds. This multidimensional approach enables the identification of flow paths in the catchments, including groundwater intrusion and surface mixing. The study aims to contribute valuable insights into the complex interplay between cryo-, hydro-, and meteorological dynamics in the High Arctic.The study was carried out with the SONATA BIS project financed by the Polish National Science Centre (grant no. 2020/38/E/ST10/00139). Keywords: UAV, Zenmuse H20T, Land Surface Temperature, Hornsund, Spitsbergen