bluuebunny/crossref_metadata_2025 huggingface dataset:
First-order global stress patterns inferred from hierarchies of upper mantle flow models
Authors: Hayek Valencia Jorge Nicolas , Stotz Ingo Leonardo , Bunge Hans-Peter , Carena Sara , Ghelichkhan Sia
Published: March 2024

Understanding the intricate dynamics of mantle flow and their influence on lithospheric stress patterns is critical for assessing reservoir responses to potential CO2 or nuclear waste storage, as well as for hazard and risk assessment. Stress patterns play a first-order control in the mechanical response within inherited tectonic structures, with varying stress sources governing different spatiotemporal scales. Our understanding of the present-day mantle flow state has much improved over the past decades, reflected in models that are consistent with first-order features. The World Stress Map (WSM) project serves as a primary observational dataset to validate our understanding of Earth's dynamics through a global compilation of crustal stress indicators.Here we study mantle flow models as a simplified superposition of Couette and Poiseuille flow types, which have been useful in explaining sub-continental scale deformation in the lithosphere. We aim to understand the role of mantle flow as a stress driver by generating stress fields from an analytical representation of upper mantle flow, derived from the superposition of steady-state flow models. Our approach allows us to build first-order expectations and conduct fast hypothesis testing for upper mantle flow states.
Unraveling the Dynamics of Irrigation Water Use in North China: Patterns and Influencing Factors over the Early 21st Century
Authors: Long Di , Zhang Caijin , Cui Yingjie , Dong Liang
Published: March 2024

Abstract: The Haihe River Basin (HRB) in North China, characterized by a warm and humid environment, has witnessed a transformation in agricultural water supply patterns, influenced by both climatic changes and groundwater withdrawal restrictions. Despite the impact of these changes on irrigation activities, comprehensive monitoring of irrigation water use (IWU) is lacking, with existing studies predominantly focusing on the influence of irrigation on climatic factors and crop yield. Few studies address the effects of warming and humidification on IWU, and the impacts of human activities associated with groundwater withdrawal restrictions remain underexplored. This study introduces a novel IWU estimation method and examines changes in IWU across the HRB from 2003 to 2022. By quantifying the contribution of irrigation water to different destinations (evapotranspiration consumption, root zone soil water increment, and groundwater recharge), key drivers of IWU change are revealed. The accuracy of IWU estimates proves high, effectively reflecting spatiotemporal changes in irrigation activities.Results demonstrate declining trends in irrigation water intensity and the proportion of irrigation area, with changes in irrigation water intensity dominating overall IWU variations. Shifts in cropping patterns, such as the southward relocation of winter wheat planting and increased drought-tolerant corn cultivation after 2012, explain regional disparities in IWU values. The proportion of irrigation water consumed by evapotranspiration and root zone water increment was 0.58 and 0.39, respectively. Utilizing the least partial square regression method, cropping pattern changes emerge as common drivers for irrigation water intensity in the three main administrative regions (Hebei Province, Beijing, and Tianjin). Irrigation management factors prevail in Hebei Province and Tianjin, while climate factors, particularly in Beijing, play a significant role. Increased water supply and a wetter climate over the past 20 years contributed to decreased irrigation water intensity, particularly in Hebei Province and Beijing. Additionally, optimization of cropping patterns and the adoption of water-saving agriculture further reduced irrigation water intensity in the HRB. This study provides a thorough understanding of the evolving irrigation landscape and associated mechanisms in the HRB over the past two decades. The findings offer insights into combatting climate change and groundwater depletion, informing strategies for sustainable water resource management.Keywords: Irrigation water use; drivers; cropping patterns; North China Plain
The NFDI4Earth Living Handbook
Authors: Hezel Dominik C. , Rose Thomas , Finkel Michael , Gerloff Ira , Xu Jie
Published: March 2024

The NFDI4Earth – the National Research Data Infrastructure for the Earth System Sciences (ESS) – is currently building a web-based entry point – called OneStop4All – to serve ESS researchers with access to ESS data, means to process and analyse these, educational ESS material, and more. One core product of the OneStop4All is the NFDI4Earth Living Handbook (LHB). This NFDI4Earth LHB aims to structure and harmonise all information related to research data management and data science approaches in the ESS in a community-driven effort. It allows researchers, societies, funding agencies, companies, authorities, or the interested public to improve their knowledge about ESS research data, how these are best analysed and interpreted as well as managed to ensure their sustainability and reusability for future projects. The LHB has an encyclopedia-style, allowing all ESS community members to provide content on all levels and to all aspects of the LHB collaboratively. This means that, like e.g., for Wikipedia, LHB articles can be added, edited, and curated at any time and by any ESS community member, to make the LHB a living library with up-to-date information that evolves with the needs of the ESS community. An editorial board curates the LHB content. This editorial board is open for members and non-members of the NFDI4Earth alike. Its main task is supporting the authors and curating their contributions, as well as developing the NFDI4Earth Living Handbook further by e.g., optimising the editorial process, providing new features, or initiating coordinated contributions on specific topics. The LHB content is structured along the research data life cycle through "collections". Collections bundle topically related articles. They can be part of other collections, and articles – as well as collections themselves – can belong to more than one collection. This way, a network-like structure is implemented that allows to structure the sometimes heavily intertwined topics in a more appropriate way than a typical table of contents. In addition, links between articles allow for easy navigation between articles. 
A new 3D approach to linking high-resolution analysis on impurities for application to deep ice
Authors: Larkman Piers , Stoll Nicolas , Rhodes Rachael , Barbante Carlo , Stenni Barbara , Lee Geunwoo , Zeppenfeld Chantal , Fischer Hubertus , Šala Martin , Bohleber Pascal
Published: March 2024

Ice that will be extracted from close to the bedrock of the Antarctic ice sheet during the Beyond EPICA Oldest Ice (BE-OI) project is expected to have more than 14,000 years of climatic information contained in a single vertical meter of ice. High-resolution analysis is required to extract meaningful climate signals from the impurities contained in this ice. This analysis should be comparable to the currently established continuous flow analysis (CFA) approach, which acquires a 1-dimensional impurity signal at approximately centimetre resolution. To date, it has been shown that smoothed high-resolution profiles taken using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) are comparable to CFA signals. However, the physical origin of this link needs to be better understood, especially in view of the imprint of the impurities on the ice crystal matrix recently revealed by 2D imaging on the micron scale.Here we present a framework to generate and explore 3-dimensional models mapping the location of soluble impurities within ice samples. This framework helps link experimentally acquired smoothed LA-ICP-MS profiles, 2-dimensional LA-ICP-MS maps of impurities, and CFA data. The conceptual step into 3-dimensions allows exploration of the distortions of the climate signal due to interactions of impurities with the ice matrix. In shallow ice with relatively small grains and well resolved stratigraphy, this distortion is likely not significant enough to compromise analysis that takes large sample volumes with large mixing, such as seen during CFA. In order to extract signals in deep ice with large crystal sizes and dense layering, where this distortion will be most relevant, we find that carefully designed LA-ICP-MS experiments coupled with post-processing upscaling techniques are required. For a test against experimental data, this work is now being applied to a comparative study involving Antarctic ice measured with both CFA and LA-ICP-MS systems to prove its application to shallower, better-understood ice intervals. Ultimately, the goal is to develop a combination of cm-scale CFA, micron-scale LA-ICP-MS imaging and 3D modelling that will provide key insight on the impurity-related climate signals in deep ice at the BEOI core and elsewhere.
Forecasting landslide motion with EXplainable Machine Learning models: the use case of Séchilienne landslide (French Alps) to identify the relevant predicting variables
Authors: Maillard Olivier , Bertrand Catherine , Malet Jean-Philippe
Published: March 2024

Recent works on landslide displacement forecasting using machine learning or deep learning models show relevant performance. However, they are mostly based on the use of historical displacement information and do not provide information on the most predictive features in terms of meteorological and hydrogeological variables for the forecast, and thus the identification of possible precursory factors. In this context, providing approaches based on EXplainable Machine Learning (XML) is essential for landslide forecasting as it concerns making decisions about risk mitigation actions, it supports the identification of possible precursory factors and it increases confidence in the predictions.The proposed XML-based landslide forecasting approach is developed and tested using ensemble learning methods such as Random Forest and XGBoost. It relies on the use of multi-year and multi-parameter data chronicles to analyse the relationships between surface displacements (target data) and hydro-meteorological conditions (predictor data). Displacement and meteorological data are acquired through the landslide monitoring network. Hydrological data, when not available, are simulated discharge calculated with reservoir based-model; the simulations allow to construct water level time series for each water reservoirs identified in the unstable slope. The predictive time series are decomposed into a set of 340 descriptive features (mean, variance, difference, number of rainy days, number of consecutive rainy periods of X days, …). The displacement time series are detrended using the multiplicative decomposition method.This method has been applied to several use cases, such as the Séchilienne landslide located southwest of the Belledonne massif (French Alps). The Random forest and XGBoost models are trained and tested over periods of 12 and 5 years respectively, and applied to three automatic extensometers located in the most active part of the landslide. The results indicate that the main features used include variations in water levels over past 10 to 30 days, as well as the number of consecutive rainy period during the month. These results are associated with accurate predictions for the three extensometers, with coefficients of determination ranging between 0.37 and 0.46.We show that these models have high predictive power while informing about the most important hydro-meteorological features. The application of the models to trendless displacement time series significantly improves prediction accuracy.
A re-analysis of equilibrium chemistry in five exoplanets' atmosphere
Authors: Panek Emilie , Beaulieu Jean-Philippe , Drossart Pierre , Venot Olivia , Changeat Quentin , Al-refaie Ahmed F. , Gressier Amélie
Published: March 2024

Studying chemical composition is fundamental to modeling the formation history of planets and planetary systems. With the first JWST data and the upcoming Ariel satellite, we expect a leap forward in the exoplanet’s atmosphere field. That’s why we propose here an analysis of five targets to improve the determination of their composition and the chemical mechanisms that take place in their atmospheres, combining multiple instruments and using retrieval methods. Our five targets are HAT-P-12b, HD 209458b, WASP-6b, WASP-17b, and WASP-39b, which have temperatures ranging from 1000K to 1700K and radii ranging from 0.9 to 1.9 Jupiter radius.
We use spatially scanned observations from the grisms G102 and G141 of the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope, with a wavelength coverage of 0.8 to 1.7 microns. We analyze these data with the publicly available Iraclis pipeline (Tsiaras et al. (2018)). We added to our datasets Space Telescope Imaging Spectrograph (STIS) observations, reduced and presented in Sing et al. (2015), to increase our wavelength coverage from 0.4 to 1.7 microns. We then perform a Bayesian retrieval analysis with the open-source TauREx (Al-Refaie et al. (2019)) using a nested sampling algorithm. We perform the retrieval considering molecular abundances varying freely and with equilibrium chemistry ACE (Agúndez et al. (2012, 2020)) and FastChem (Stock et al. (2018)). The principle is to start from a transmission spectroscopy dataset and to seek the model that characterizes the atmosphere in the most probable way, in other words, the spectrum that best fits the data points. This work is detailed in a dedicated paper published in the Astronomy \& Astrophysics journal (Panek et al. (2023)).
For very hot planets, thermochemical equilibrium may be close to reality, but for less hot planets, vertical mixing and photodissociation bring these planets out of equilibrium. That's why our results will soon be extended to a non-equilibrium thermochemistry model (Venot et al. (2020)).
Simulation-based inference as a paradigm for scientific machine learning in the cryosphere and beyond
Authors: Groenke Brian , Aalstad Kristoffer , Pirk Norbert , Westermann Sebastian , Zscheischler Jakob , Gallego Guillermo , Boike Julia
Published: March 2024

Mechanistic or dynamical models based on governing equations are ubiquitous throughout science and engineering. Such models, also referred to as “simulators”, are typically characterized by a forward mapping from some set of inputs or parameters to one or more output quantities of interest. In many cases, the inputs required for the forward model are either unknown or represent approximations of unresolved processes. Bayesian inference provides a natural framework for constraining this model uncertainty using observed data. Such a framework is especially valuable in cryospheric application domains such as permafrost research, where direct observations of many quantities of interest, e.g. subsurface temperature and soil moisture, are only sparsely available. Mechanistic models based on known physics therefore play an indispensable role in filling these gaps. However, virtually all methods for Bayesian inference require repeated evaluation of the forward model which is often computationally challenging, especially for dynamical systems. As a result, computational requirements of statistical inference very quickly become intractable even for systems of only moderate complexity. The burgeoning field of “simulation-based inference” (SBI) aims to leverage modern computational methods from machine learning (ML) and data assimilation (DA) to overcome these challenges and facilitate large scale uncertainty quantification in complex scientific models. In this work, we show how SBI can be seen as a unifying theoretical framework that bridges the gap between existing DA methods (e.g. variants of the ensemble Kalman filter) and full-fledged Bayesian inference with the goal of facilitating hybrid statistical-physical modeling of complex systems. We present a novel set of software tools for making SBI more accessible to researchers along with benchmarks of several methods drawn from both the ML and DA literature. Two of these benchmarks are based on use cases from Arctic land surface modeling: degree day approximation of snowmelt and geothermal inversion of historical climate change from boreholes in Arctic permafrost. We highlight the contributions that SBI can make to solving such inverse problems and discuss further potential applications in the cryosphere and beyond.
Phenological variability between species of floodplain forests and the role of the pollinators
Authors: Bartošová Lenka , Dížková Petra , Bauerová Jana , Balek Jan , Křenová Zdenka , Svobodová Eva , Bláhová Monika , Hájková Lenka , Možný Martin , Žalud Zdeněk , Trnka Miroslav
Published: March 2024

The impact of climate change on phenological timing is well-known and described in the scientific literature. Most papers agree that rising temperature accelerates the onset of spring phenological phases. Nevertheless, there is more significant disagreement in the synchronicity or asynchronicity of phenological trends of individual species. In this study, therefore, we will work with long-term phenological data that have been observed continuously in floodplain forests (and relate to herbs, shrubs, trees, and bird populations) from 1961 to the present. The observed plants and bird species showed statistically significant (p < 0.05) shifts in phenological terms to an earlier year. Still, the rate of the shift among the observed species differed. The most progressive shifts were detected for the herbs (2.3 days per decade), followed by the shrubs (2.2 days per decade), trees (1.4 days per decade), and finally, the bird species (also 1.4 days per decade). There are many changes in trends within the specific species group – e.g., quite a considerable variability was detected for herbs – the phenophase ´full flowering´ trend is moving between 1.3 and 3.3 days per decade. For this reason, we also include the role of pollinators within this study - bumblebees (Bombus) as a new input to the phenological observations to see if there is any asynchrony between these early spring species. Acknowledgment: This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic (grant AdAgriF - Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation (CZ.02.01.01/00/22_008/0004635).
May and August 2023: the extreme landslide events in Slovenia, triggered by extreme rainfall
Authors: Peternel Tina , Jemec Auflič Mateja , Šegina Ela , Turk Domen , Jež Jernej , Bavec Miloš
Published: March 2024

In 2023, Slovenia experienced two major natural disasters caused by prolonged and intense rainfall event. The first occurred in May 2023 with over 2,000 shallow landslides triggered by prolonged heavy rainfall that lasted for about three weeks (from May 5 to May 23). This event mainly affected the north-east of Slovenia. The landslides caused major material damage to agricultural land and infrastructure, and at least 10 houses were evacuated.The most notable event occurred in August 2023 and was estimated to be one of the largest in the history of independent Slovenia. In the period between August 3 and 6, 2023, precipitation with heavy storms and intense rainfall covered almost all of Slovenia. The extreme rainfall led to widespread flooding and triggered numerous landslides.We estimate that there were around 10,000 landslides across Slovenia, with a particularly high density in some areas. Due to hydro-meteorological conditions (increased water flow and rising groundwater levels), a large portion of the landslides turned into mud or debris flows and were deposited a few to several hundred meters away from the source area. The main reason for the extreme landslide disasters was the heavy rainfall and high soil moisture as a result of the rainfall in July.Due to the large scale of the landslide disaster in August, a detailed damage assessment is still being carried out. Preliminary estimates by the Geological Survey of Slovenia (GeoZS) indicate that around 10,000 landslides occurred (with an area of 1,000 to over 75,000 m2) and caused damage of more than €3 billion, of which around 40% had a direct impact on the built environment.In both cases, the GeoZS emergency service provided residents and the relevant authorities with landslide forecasts and warnings using the existing national MASPREM system (Slovenian Landslide Forecast and Warning System). Although the Slovenian LEWS is operational, the performance of the forecasts has shown that about 15 to 25 % of the warnings were false, which we attribute to the short period of antecedent percipitations (the current calculation takes into account 3 days of antecedent percipitations)  and lack of soil and hydrological related parameters (e.g. effective percipitations, soil moisture, etc.).Fundings:This research was funded by the Slovenian Research And Innovation Agency through research projects J6-4628 and programme P1-0419. Additional financial support was provided by the Ministry of Natural Resources and Spatial Planning, Ministry of Defence (through project MASPREM) and project “Development of research infrastructure for the international competitiveness of the Slovenian RRI space – RI-SI-EPOS” (co-financed by the Republic of Slovenia, Ministry of Education, Science and Sport and the European Union from the European Regional Development Fund).
Long-Term Legacy of Climate Overshoot on Economic Productivity: An Emulator-Based Modeling Approach
Authors: Schleussner Carl-Friedrich , Schöngart Sarah , Schwartz Moritz , Schwaab Jonas , Pretis Felix
Published: March 2024

Most climate change mitigation scenarios aimed at limiting end-of-century warming to 1.5°C involve overshoots, that is they temporarily exceed 1.5°C of warming. Despite the prevalence of overshoot pathways, their effects on economic productivity have not been systematically assessed yet. Furthermore, existing assessments of future economic risks do not systematically explore the full spectrum of physically plausible outcomes under given emission pathways and thereby run the risk of underestimating high-end risks. In this study, we rely on coupled climate model emulators representing the full physical climate uncertainty chain to assess the GDP per capita growth under a range of policy relevant emission scenarios, seven of which are characterized by overshoot.
Using the emulators FaIR and MESMER, the emission scenarios were translated into a large ensemble of spatially resolved annual temperatures that captures both model uncertainty and natural variability on both global and local scales. Building on standard approaches to empirically estimate the effect of temperature on GDP per capita growth, we incorporate parametric uncertainties in the economic response and link these empirical estimates with the overshoot scenarios. The resulting dataset allows for the examination of local and regional impacts (and associated uncertainties) of overshoot scenarios on economic productivity, including the timing and magnitude of temperature threshold exceedance.
We find a legacy of overshoot in future GDP gains way beyond the end of the temperature overshoot. We also report heavy tailed risks of economic damages when considering the full range of plausible physical outcomes. Under all but the most stringent scenarios there is a non-negligible risk for near-stalling of 21st century per capita growth for particularly vulnerable countries.
We find that near-term warming rates (2020-2040) play a pivotal role in shaping future GDP per capita gains. Across overshoot scenarios, by 2100 GDP per capita levels are lower with rising warming rates, while the magnitude of the GDP per capita gain is linked to the extent of the overshoot. Our results highlight the critical importance of near-term emission reductions to limit economic risks posed by climate change over the 21st century. A temperature overshoot poses substantial risks of a long-term legacy of economic damages for decades to come.
Observation of fluorescent primary biological particles at the North Pole: A case of inter-coupled system behaviour?
Authors: Asplund Julia , Ekman Annica ML , Freitas Gabriel , Granskog Mats A. , Heutte Benjamin , Lapere Remy , Muilwijk Morven , Naakka Tuomas , Schmale Julia , Thomas Jennie , Zieger Paul
Published: March 2024

Aerosol-cloud interactions remain among the most uncertain key parameters in the fast-changing Arctic climate system. Arctic clouds often consist of both liquid droplets and ice crystals, the abundance of which is constrained by the availability of ice nucleating particles (INP). We present observations of fluorescent primary biological aerosol particles (fPBAP), shown to be potent INP, obtained during the Arctic Ocean 2018 expedition onboard the Swedish icebreaker Oden in August- September of 2018, at the North Pole. The fPBAP were recorded on a single-particle level using a Multiparameter Bioaerosol Spectrometer, as a part of a complete setup for measuring physical and chemical aerosol properties.  Potential sources of fPBAP during an extended period of high concentrations are investigated using a combination of auxiliary measurements, trajectory analysis, remote sensing data, ocean biogeochemistry reanalysis data, and model experiments with WRF-Chem. Our evidence suggests that the observed case of increased fPBAP concentration at the North Pole was caused by transport of fPBAP enriched marine aerosol from a source within the Arctic region, but in open water south of the pack ice. We also highlight how future interdisciplinary efforts can be used more efficiently to improve the source mapping of Arctic fPBAP, which is needed to assess their overall climate-relevance in the polar regions.
Connectivity Anisotropy of fracture network: A connected branch approach
Authors: Gairola Pradeep , Rawat Vaasudev , Bhatt Sandeep
Published: March 2024

Fracture networks play a crucial role in various natural processes, including fluid flow in subsurface reservoirs, seismicity, and the overall mechanical behavior of rocks. Understanding the anisotropic nature of connectivity within these networks is essential for accurately modeling and predicting subsurface processes. By adopting a connected branch approach, the study explores the directional variation of branches within these networks which shed light on the preferential pathways and their impact on overall connectivity. This approach enables a comprehensive analysis of the network's structural complexities, providing valuable insights into the anisotropic behavior of fractures.Our study employs fractal geometry to quantify the connectivity anisotropy of fracture networks. Fractal dimensions are a powerful tool to characterize the intricate and self-repeating patterns inherent in fracture distributions. By applying these dimensions in different orientations, we reveal the directional variations in connectivity, providing a comprehensive analysis of the network's anisotropic behavior. The results contribute to the fundamental understanding of geological processes and have practical implications for various industries, such as oil and gas exploration, geothermal energy extraction, and underground waste storage. This research presents a step forward in unraveling the complexities of fracture networks, offering valuable insights for improved reservoir characterization, enhanced resource recovery, and more accurate subsurface fluid flow predictions in geoscience and engineering applications.
Revisiting hydrological modelling strategies for low flow simulations in the French Moselle catchment in response to recent hydrological droughts
Authors: El Khalfi Hajar , Delus Claire , Drogue Gilles , Grelier Benjamin , Lebaut Sébastien , Manceau Luc , François Didier
Published: March 2024

In recent years, the French part of the Moselle catchment (10 770 km²) has experienced successive severe hydrological droughts. These droughts are characterised by an unprecedented pattern of slow and prolonged periods of recession, reflecting a shift from the typical drought behaviour observed in the past. This difference from past droughts presents a challenge and requires a new approach to adapt hydrological modelling methods for low flow simulations in the context of climate change. A seven free parameters model originally developed for low flow simulation called PRESAGES is considered in this study. Three reservoirs (a production reservoir, a delayed discharge reservoir and a groundwater reservoir) are implemented to manage both internal and external flows and model states. We observed that the current version produces poor simulations in our study area on recent observed discharge values particularly during long recession phases over the period 2010 to 2020. This period has indeed been characterised by severe low flow events with frequent and persistent recessions. In order to improve the efficiency of our model, we propose a non-linear depletion approach, specifically the Wittenberg model, instead of the conventional linear depletion law used in the current version of the model. The reason for this modification is that the linear depletion law oversimplifies the physical processes governing the depletion of groundwater reservoirs by ignoring inherent non-linearities. As a consequence, flow simulations no longer represent the reality of observed events. Another aspect addressed to enhance the model performance is the search of an objective function that, in conjunction with the new model structure, takes account a severity increasing over time. To this end, different mathematical transformations of the single objective function based on the Kling-Gupta efficiency criterion (KGE) were compared to calibrate the PRESAGES model. Benefits from this new modelling strategy was assessed using numerical criteria and visual inspection of hydrographs. The results indicate that employing the power of -0.5 as the objective function transformation to calibrate the model coupled with the non-linear depletion law for the groundwater reservoir makes the model more competitive. Model’s ability to simulate and capture prolonged and slow phases of recession was particularly improved. These results demonstrate the necessity to cope with the progressive aridification of the study area in modelling strategies of water resources.
 
 
The Diurnal Evolution of Atmospheric Boundary Layers in the LIAISE Field Campaign 
Authors: Mangan Mary Rose , Vila-Guerau de Arellano Jordi , van Stratum Bart , Lothon Marie , Canut Guylaine , Hartogensis Oscar
Published: March 2024

The Land surface Interactions with the Atmosphere over the Iberian Semi-arid environment (LIAISE) field experiment took place in July 2021 in the Ebro River Valley in the northeast of Spain. In the domain of the LIAISE field campaign, thermal surface heterogeneity is induced by irrigation which was applied to agricultural fields that cover ∼65% of the LIAISE region contrasting with the remaining 35% of the region covered by non-irrigated agricultural fields. Observed Bowen ratios reach approximately 20 in non-irrigated fields, while observed Bowen ratios are approximately 0.1 in the irrigated fields. This contrast could lead to an interaction of scales that range from the a regional scale that encompasses both the irrigated and non-irrigated areas (∼10 km) down to a scale of an individual field (∼100 m).  In addition to surface fluxes, profiles of both the mean state of the atmospheric boundary layer (ABL) and turbulent transport in the ABL were measured over both the irrigated and non-irrigated landscapes. Observations confirm that the surface heterogeneity is felt most strongly near the surface; however, approximately 1000 m above ground level, there appears to be a blending height in which heterogeneity mixes so that the observed ABL potential temperature and specific humidity profiles are similar over both landscapes. Conversely, profiles of turbulent transport shows notable differences between the irrigated and non-irrigated boundary layers. Buoyancy flux over the irrigated area is driven by moisture fluxes, and above an internal boundary layer (approximately 25% of the non-irrigated ABL height), turbulent fluxes of scalers reach their maximum. Turbulence kinetic energy is higher over the non-irrigated landscape because of the increased buoyancy from the surface sensible heat flux, and the observed ABL heights are 100-500 m higher in the non-irrigated landscape than the irrigated landscape. In this study, we discuss novel experiments that combine the synoptic- and meso-scale forcing (ERA) with the explicit simulation of secondary circulations driven by surface heterogeneity using large-eddy simulation (LES).   The surface of the LES is defined with prescribed sensible and latent heat fluxes from observations. With the LES, we aim to better understand the development of the ABL over the LIAISE domain and how the ABL differs in space between the irrigated and the non-irrigated areas Furthermore, we focus on the turbulent transport – both vertically and horizontally in space – to illustrate the most important processes which contribute to the locally observed ABL.
Tephra comminution by vehicles and resuspension of volcanic ash: impact on ambient air quality in urban areas
Authors: Tomašek Ines , Tournigand Pierre-Yves , Andronico Daniele , Eychenne Julia , Horwell Claire , Kueppers Ulrich , Taddeucci Jacopo , Claeys Philippe , Kervyn Matthieu
Published: March 2024

Tephra fallout is a common feature of different styles of eruptions and is the most widespread of volcanic hazards. Fallouts containing substantial amounts of fine-grained particles pose a concern for human health since exposure to respirable PM (sub-10 μm, i.e., PM10) is associated with adverse health effects. The products of effusive or poorly explosive events (e.g., lava fountains, strombolian eruptions) and their impacts are less researched as they typically generate rather coarse-grained deposits. Yet, reworking of tephra deposits by wind, traffic or other human activities can potentially alter the initial grain size distribution of a deposit and generate finer material. Remobilisation of such reworked deposits can affect ambient air quality (i.e., PM10 levels), thus leading to an increased exposure hazard.In this study, we conducted in situ experiments on the slopes of Etna volcano, Italy, which frequently covers neighbouring urban areas with coarse-grained basaltic tephra. We aimed to understand the changes in tephra grain size distribution and airborne PM10 concentration associated with vehicular activity. For this purpose, we drove a small SUV-type car over an area of a road that we covered with tephra, and investigated the outcomes as a function of 1) the number of car passages (between 10 and 70), 2) the starting thickness of the tephra deposit (between 2 and 10 mm) and 3) vehicle speed (between 20 and 50 km/h). The results show that the grain size of the original tephra deposit decreases with the number of car passages, most notably with higher vehicle velocity (50 km/h) and increasing deposit thickness. Airborne PM10 increased with a higher number of car passages, but also with increased tephra thickness and increased vehicle speed. Our observations have important implications for the management of tephra fallouts in urban areas. We have shown that vehicles will change the grain size distribution of basaltic ash by comminution so local communities can expect that, after an eruption, concentrations of PM10 may increase with time and affect exposures close to roads.
Advancing Inland Water Body Mapping with Self-Supervised Machine Learning
Authors: Sharma Ankit , Narayanan Mukund , Ilampooranan Idhayachandhiran
Published: March 2024

Traditional methods of mapping inland water bodies involve labor-intensive and manual data labeling, limiting their scalability to a larger extent. This study introduces a novel approach: self-supervised machine learning (SSML) for mapping inland water bodies. SSML is a training method where a model learns from data without the need for explicit human-labeled annotations. Using this technique, the study mapped inland water bodies in Pudukkottai, India, using LANDSAT-8 imagery from 2021. The training data for SSML were derived from two spectral indices: the Normalized Difference Vegetation Index and the Modified Normalized Difference Water Index. These indices were used to establish a threshold for automatically generating pseudo labels for two categories: water and non-water. This pseudo-labeled dataset was then utilized to train various machine learning models, including random forest, support vector machine, classification and regression tree, and gradient boosting. The accuracy of the final classified map was assessed using a spatial agreement test, which measures the degree of agreement of the classified map in relation to a reference dataset. The spatial agreement test used the Joint Research Commission (JRC) water map of 2021 as the reference dataset. The final inland water body map, derived from the SSML approach, demonstrated a high spatial agreement of 91% with the JRC water map. Among the SSML models, the random forest model outperformed others due to its ensemble nature. Compared to traditionally supervised classifiers (trained with 137 water points and 74 non-water points), the SSML models exhibited superior performance with a spatial agreement of 91%, significantly higher than the 67% achieved by the supervised model. This study is the first to demonstrate the application of SSML for mapping inland water bodies, offering an efficient and cost-effective alternative to traditional manual labeling. This approach holds significant potential for advancing remote sensing applications, particularly in regions where obtaining ground truth labeling is costly or impractical.
Resolving bedload flux variability
Authors: Pähtz Thomas , Chen Yulan , Xie Jiafeng , Monthiller Rémi , Maurin Raphaël , Tholen Katharina , Ho Hao-Che , Hu Peng , He Zhiguo , Durán Orencio
Published: March 2024

Bedload transport plays a vital role in shaping Earth’s environment by promoting the formation and growth of geological features of various scales, including ripples and dunes, deltas and fans, and laminations and cross-bedding. A key problem hampering our understanding of bedload-induced landscape evolution is the notoriously large variability commonly associated with measurements of bedload flux, even under controlled and highly idealized conditions in the laboratory, such as fully-developed, unidirectional open-channel flows over flat beds composed of grains of nearly uniform sizes. For example, two recent experimental studies report a nearly sixfold different nondimensionalized bedload flux at a comparable Shields number for spherical grains [1, 2]. The likely culprit is the immense difficulty experimentalists face in estimating the transport-driving bed shear stress. There is currently no universally accepted method of even determining the bed surface elevation in the presence of bedload transport, which is particularly problematic for shallow flows where small changes have a large effect. Neither is there agreement on how to account for the effects of sidewall friction, which become the stronger the smaller the width-to-depth ratio b/h of the open-channel flow. Standardly employed empirical sidewall corrections have arguably a greater resemblance to cooking recipes than to formal physically-derived methods. In addition to such experimental difficulties, there is the physical question of how grain shape, which usually is not controlled for in laboratory experiments, affects bedload flux. A recent prominently published study argued that grain shape is the predominant reason for bedload flux variability and put forward a semi-empirical, analytical bedload transport model to account for it [1].Here, we compile data from existing experiments and existing and new DNS-DEM, LES-DEM, and RANS-DEM numerical simulations of turbulent bedload transport of shape-controlled grains, in which b/h varies between 0.1 and infinity (periodic boundary conditions in simulations). After employing a non-empirical sidewall correction, which we derived from the phenomenological theory of turbulence, and a granular-physics-based method to determine the bed surface elevation, all data for spherical grains of sufficient size collapse onto a single curve, resolving the experimental problem of bed shear stress determination. Furthermore, the combined data for spherical and non-spherical grains are in strong disagreement with the model of Ref. [1] but support our alternative analytical bedload model across grain shapes, bed slopes, flow strengths, and channel widths.[1] Deal et al., Nature 613, 298 (2023). https://doi.org/10.1038/s41586-022-05564-6[2] Ni & Capart, Geophysical Research Letters 45, 7000 (2018). https://doi.org/10.1029/2018GL07757
North Atlantic SST variability during strong winter extratropical cyclones
Authors: Liberato Margarida L. R.
Published: March 2024

Extreme weather and climate events, such as extratropical cyclones and droughts, represent a topic of paramount importance in the Iberian Peninsula and the North Atlantic Ocean plays an important role in shaping their frequency and intensity. Sea surface temperature (SST) variations, which are important indicators of ocean variability, can result in anomalous diabatic heating or cooling of the overlying atmosphere. In this study, the contributions of different physical processes to the development of North Atlantic explosive extratropical cyclones (EC) affecting the Iberian Peninsula are investigated using the ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF). Results suggest that the North Atlantic Ocean SST contributed to the formation and intensification of extratropical cyclones, and particularly to the formation and development of intense storms. Furthermore, the combined analysis of SST and net surface heat flux (QN) also shows the cooling of the ocean associated with the EC tracks caused by the heat exchanges between the ocean and the atmosphere.
 
Acknowledgements
This work is supported by national funds by FCT - Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020 (https://doi.org/10.54499/UIDB/04033/2020).
 
A systematic investigation of urban modifications of mixed layer height and cloud cover in Berlin, Germany
Authors: Fenner Daniel , Christen Andreas , Glazer Russell , Grimmond Sue , Kotthaus Simone , Looschelders Dana , Meier Fred , Morrison William , Zeeman Matthias
Published: March 2024

In order to better understand how urban areas modify the regional atmospheric boundary layer (ABL) and to improve and evaluate weather and climate models for urban applications and services, detailed ABL observations are needed. With new instrument technologies and advanced automatic algorithms for detection of aerosols, mixed-layer height (MLH) and boundary-layer clouds, ground-based remote sensing instruments are increasingly used in urban observational networks.During a one-year measurement campaign in Berlin, Germany (urbisphere-Berlin, Autumn 2021 – Autumn 2022), a variety of ground-based ABL observations were carried out in the greater Berlin region. Berlin as an isolated continental city with approximately 3.8 million inhabitants provides a fairly homogeneous rural background. The urbisphere network included five inner-city, six outer-city and 14 rural sites equipped with continuously-operated Automatic Lidar and Ceilometers (ALC). The measurement network was designed and set up in a systematic and rigorous manner in order to capture intra-urban, urban-rural, and upwind-city-downwind effects of MLH, cloud-base height (CBH), and cloud cover fraction (CCF) along several transects as air masses move over the city. Based on the ALC observations, MLH, CBH and CCF were automatically derived. ALC observations are complemented by measurements of wind and temperature profiles over the city using Doppler-Wind Lidars and radiosondes concurrently released in urban and rural locations during selected days. Surface heat fluxes are continuously measured with six eddy-covariance flux towers and seven path-averaging scintillometers in urban and rural settings.This contribution highlights the scientific considerations of the systematic measurement network design and the corresponding data analysis. We are proposing a scheme of attributing measurements to rings around the city centre representing the inner city (radius of 6 km), the outer city (radius of 18 km) and rural areas (radius of 90 km), further separated into upwind, downwind and other sectors. A detailed statistical analysis of the year-long dataset finds differences in MLH, CBH and CCF during different seasons and under different weather forcings. Selected case-study days are analysed in more detail to understand the processes controlling the interactions between surface fluxes and mixed-layer dynamics. These days are further used to evaluate the forecasting skill of hectometric dynamical-modelling runs with regard to ABL dynamics, quantifying also the sensitivity of ABL dynamics in the model to surface representation (e.g. soil moisture, heat flux partitioning).
Assessing changes in winter sea ice deformation – from MOSAiC to the Fram Strait
Authors: Itkin Polona , Divine Dmitry
Published: March 2024

During winter, sea ice is moving in cohesive clusters of ice plates. These clusters – hereafter named ‘Coherent Dynamic Elements’ (CDE) are composed of several areas of deformed and level ice, that slide coherently along active sea ice fractures. The largest sea ice fractures detectable from medium resolution Synthetic Aperture Radar (SAR) satellites (about 50 m spatial resolution) are the Linear Kinematic Features (LKFs). Sea ice deformation information can be estimated from the strain rates in the LKFs and as well from the geometrical characteristics of the CDEs. However, there is a sudden seasonal transition, at the point where the sea ice warms and loses its internal strength. After this transition the delineation of LKFs and CDEs from SAR becomes challenging. In this contribution we will analyze sea ice deformation during the drift of the MOSAiC expedition from October 2019 to July 2020. During this time, the expedition drifted the entire length of the Transpolar drift from the northern Laptev Sea into the Fram Strait and the sea ice surrounding it underwent numerous deformation events. The MOSAiC sea ice deformation data and the onset of the melt period is compared to the data over the Fram Strait, where the sea ice deformation can was estimated from SAR and upward looking sonar devices on fixed moorings for the period of 2010-2023. We will present the data on the changes in the onset of the melt period and show that MOSAiC year was a typical year representative for the sea ice deformation of the recent decade.