bluuebunny/crossref_metadata_2025 huggingface dataset:
Compaction pressure goes global: Investigating fluid release and flow in subduction zones worldwide
Authors: van Keken Peter E. , Wilson Cian R. , Abers Geoff A.
Published: March 2024

Subduction of oceanic slabs causes the influx of fluids through hydrated phases. Fluids are released by metamorphic dehydration reactions particularly when the slab comes in contact with the hot mantle wedge at depths greater than ~80 km. Fluid release can be diverse and occur at different depths inside the oceanic slab with sediments and uppermost oceanic crust generally dehydrating before the serpentinized mantle and gabbroic sections.Significant progress has been made in recent years on geophysical imaging of subduction zones that highlight the thermal structure, the location of metamorphic dehydration reactions, and the presence of fluids in slab and mantle wedge (e.g., Kita et al., Tectonophysics, 2010; van Keken et al., Solid Earth, 2012; Shiina et al., GRL, 2013; Pommier and Evans, Geosphere, 2017, Abers et al., Nature Geoscience, 2017). In a complimentary fashion, geodynamical modeling provides first principles constraints on how fluids are released and transported.Using a simplified modeling geometry, Wilson et al. (EPSL, 2014) showed the importance of compaction pressure gradients as an oft cited, but also frequently ignored, driving force for fluids in the slab. The inclusion of compaction pressure gradients causes the fluids to both be driven from their source to the arc and flow up in part parallel to the slab surface, explaining to at least some extent geophysical observations.We have modeled the effects of compaction pressure gradients in a global set of subduction zone models (van Keken and Wilson, PEPS, 2023) and show that focusing of the fluids below the typical arc location (at where the slab is at about 100 km depth) is a common feature and that therefore the compaction pressure effects, along with the geometry of the cold corner in the mantle wedge, can naturally explain the position of the arc above subduction zones globally.
Measuring Significant Wave Height fields in two dimensions at kilometric scales with SWOT
Authors: Bohe Alejandro , Chen Albert , Chen Curtis , Dibarboure Gerald , Dubois Pierre , Fore Alexander , Hajj George , Legresy Benoit , Lenain Luc , Molero Beatriz , Peral Eva , Raynal Matthias , Stiles Bryan
Published: March 2024

The Surface Water and Ocean Topography (SWOT) mission is primarily designed to measure Sea Surface Height in two dimensions at an unprecedented resolution thanks to its innovative Ka-band radar interferometer KaRIn. In addition to the topography measurements derived from the phase difference between the images acquired at each of the two antennas separated by 10 meters, KaRIn can also provide information about the sea state, by exploiting the measured power in each of the SAR images and the interferometric correlation between both acquisition channels.This last quantity, sometimes referred to as interferometric coherence, is directly affected by the presence of surface waves. This provides a fantastic opportunity to measure, for the first time at a global scale, Significant Wave Height at kilometric resolutions (well below the reach of nadir altimeters) and in two dimensions. This, however, requires estimating all other sources of decorrelation of instrumental origin with an exquisite precision to avoid misinterpreting instrumental effects as geophysical signals.In this talk, I will briefly describe how the interferometric acquisitions by KaRIn are calibrated and processed to obtain SWH maps in 2D at various km-scale resolutions (typically 2x2 km or 5x5 km), and discuss how the accuracy at which we need to estimate all the other sources of decorrelation varies with cross-track distance and actual SWH to highlight the most challenging regimes for the inversion. I will then present comparisons between the KaRIn two-dimensional SWH measurements and several independent sets of validation data, including data from SWOT’s nadir altimeter, from the SAR nadir altimeter on-board Sentinel-3, from MASS’s lidar, and from in-situ data. I will finish by discussing various physical features that can be observed in the retrieved SWH fields to illustrate that the high resolution and the two-dimensional character of SWOT measurements really open the door to the quantitative study of the processes that contribute to sea-state variations at small scales.
Physiological, yield and nut quality responses of walnut tree subjected to different irrigation regimes following IRRIFRAME water balance model
Authors: Perulli Giulio Demetrio , Gentile Salvatore Luca , Solimando Domenico , Anconelli Stefano , Baldi Elena , Toselli Moreno , Boini Alexandra , Manfrini Luigi
Published: March 2024

In the last years commercial walnut orchards plantation is increasing in Emilia-Romagna, an Italian region renowned for its excellence in fruits cultivation. Despite the expansion of walnut plantations in this region, there is scarcity of studies focusing on the water demand of this crop. This research aims to assess the response of an adult walnut orchard (cv. 'Chandler') to three distinct irrigation treatments (100% ETc, 75% ETc, and 50% ETc). Water supply was managed according to the IRRIFRAME water balance model. Plant water status (stem water potential, SWP), leaf gas exchanges (leaf photosynthesis, A; stomatal conductance, gs), yield, nut quality (e.g., nut weight, shelled yield, kernel colour) and water use efficiency (WUE) were measured for four consecutive seasons (2018-2021). Differences in plant water status were detected only in half of the performed measurements and trees irrigated at 100% ETc generally showed more positive SWP values compared to 75% and 50% ETc trees. Gs and A were less sensitive than SWP to the different water regimes, showing limited differences among treatments only in the first two years. Yield and main nut quality parameters were slightly affected by irrigation treatments mainly in 2018 and 2019, with the 50% ETc showing a reduced productivity compared to 100% and 75% ETc. No differences where registered for shelled yield and kernel colour for all the four consecutive years. On the contrary, irrigation treatments highly affected WUE in all the considered years, with 100% ETc being the less efficient treatment, followed by 75% and 50% ETc.
Kinematic Hierarchical Filling-&-Spilling vs. fully 2D schemes for pluvial inundation
Authors: Kyaw Kay Khaing , Luzzi Valerio , Bagli Stefano , Castellarin Attilio
Published: March 2024

Urban areas are especially vulnerable to the effects of pluvial flooding due to the high population density and concentration of valuable resources as well as the fact that heavy precipitation events are becoming more common and more intense because of climate change. The use of high-resolution hydrologic-hydraulic numerical models for pluvial flood risk assessments in large metropolitan areas is still very resource-intensive. Several studies have pointed out the potential of fast-processing DEM-based methods, like Hierarchical Filling-&-Spilling Algorithms (HFSAs), considering the increasing availability of LiDAR (Light Detection and Ranging) high-resolution DEMs (Digital Elevation Models). We have developed a fast-processing HFSA, as part of a web-based digital twin solution for flood risk intelligence (see https://saferplaces.co/), that enables building-by-building pluvial flooding hazard and risk modelling in large urban areas by accounting for spatially distributed rainfall input and infiltration processes through a pixel-based Green-Ampt model. In this study, we upgrade SaferPlaces’ HFSA based on kinematic wave approximation to depression points and analysing the impact of flow contributions from one cell to another on the formulation of travel time, as well as the backwater effect in depression-related watershed areas. We present the first applications of the kinematic HFSA, comparing it with two state-of-the-art fully 2D rain-on-grid inundation models (i.e. HEC-RAS and UnTRIM). We discuss the potential and limitations of Saferplaces' upgraded HFSA and identify future research possibilities.Keywords: Hierarchical Filling-&-Spilling Algorithms, kinematic wave, pluvial flooding, rain on grid.
Water retention of soils with coarse texture under forest stands using classical method and PTFs
Authors: Katona Máté , Végh Péter , Balázs Pál , Bidló András , Horváth Adrienn
Published: March 2024

A changing climate brings more extreme weather and uneven distribution of rainfall events. These effects are already being observed, and although the average of the many annual rainfall totals is not changing significantly, the length and frequency of periods without rainfall and droughts have increased significantly. These changes are also felt by forest stands and their sensitivity to drought is a crucial factor in their growth and health. Our research team has been collecting data on the organic matter content and water-holding capacity of Hungarian soils for many years and has now also produced pF measurements. The sub-region of the Transdanubian Mountains region has provided a comprehensive picture of coarse-textured soil on which forest soil-forming processes take place. Soil samples were taken up to the limit of the occurrence of soil-forming processes, but at least up to 100 cm. Acidic Arenosols developed on the sand parent material. On these, mixed stands of beech and turkey oak-sessile oak associations developed. The pH, CaCO3 content, organic matter content, texture, and bulk density of the soils were determined under laboratory conditions in 10 cm layers. The majority of the soil samples had an acidic pH between 4.4-6.4, with fine sand and sandy loam physical texture and free of calcium carbonate, with low humus content (0.9% in the upper 40 cm) compared to forest soils. From the pF measurements and using pedotransfer functions, we determined the potential water availability for plants, and using local climate data and measured soil data, we constructed a Thornthwaite-type water balance model for the area and used it to estimate the drought sensitivity of the area. These models improve and speed up the methods and therefore its accuracy and applicability to forest soils is a primary concern of this research.The present publication was supported by the National Research Development and Innovation Fund of the Ministry of Innovation and Technology (successor: Ministry of Culture and Innovation) under the project TKP2021-NKTA-43, funded by the TKP2021-NKTA grant programme, and by the New National Excellence Programme of the Ministry of Culture and Innovation, code number ÚNKP-23-3-I-SOE-172, funded by the National Research, Development, and Innovation Fund. The field and laboratory tests were also carried out using equipment purchased with the support of the project "GINOP-2.3.3-15-2016-00039 - Investigation of the conditions for growing woody biomass".
Predator-prey characteristics of the rapid shallow-to-deep transition of atmospheric convection
Authors: Vraciu Cristian-Valer , Savre Julien , Colin Maxime
Published: March 2024

Within a diurnal cycle, the transition from shallow to deep convection takes several hours, despite having large environmental instability at the onset of shallow convection. During this period, the cloud environment remains rather steady, while the convection exhibits a rapid development. Properly predicting the timing of this rapid shallow-to-deep transition within a diurnal cycle is still a major shortcoming of weather and climate models that employ the so-called mass-flux parameterization of atmospheric convection, as they typically predict the onset of deep convection too early, not allowing for a gradual convective deepening. In this work, it is argued that the problem of correctly representing the diurnal cycle of deep convection comes from the fundamental assumptions of the mass-flux formulation, in which it is considered that the clouds, represented by steady-state plumes, only interact with a spatially homogeneous environment. However, in the rapid shallow-to-deep transition, the convection still requires several hours to deepen, even if the environment remains steady, so some interactions must be missing. Here, a conceptual model for cloud development is introduced, in which a cloud is formed due to the sum of water transport from the boundary layer by multiple updrafts during its life-time, allowing for cloud-cloud interactions. This process captures local preconditioning, in which the clouds themself provide favorable conditions for the development of subsequent updrafts. It is also argued that the cold pools act as a reinforcement of this process, organizing the updrafts, and thus, allowing for a greater degree of local preconditioning. Based on this new conceptual model, it is argued that the shallow-to-deep transition can be seen as a predator-prey problem, in which the cloud population at the cloud base acts as prey, while the surface precipitation rate acts as predators. This simple predator-prey model is then tested against an idealized large-eddy simulation, showing that indeed, the rapid shallow-to-deep transition of atmospheric convection exhibits predator-prey characteristics. Moreover, it is shown how easily the simple predator-prey model can be implemented in current mass-flux schemes, leading to improved representation of deep convection within a diurnal cycle. Overall, this suggests that better representing the spatial organisation of clouds can lead to improvements in the timing of cloud and precipitation properties, thanks to a better convective memory.
Seismic hazard related to deep geothermal operations (Part II) : iterative methodology for hazard assessment 
Authors: Maury Julie , De Santis Francesca , Peter-Borie Mariane , Klein Emmanuelle , Dominique Pascal , Contrucci Isabelle
Published: March 2024

Ineris and BRGM published a good practices guide and recommendations for the management of seismicity induced by deep geothermal energy operations. It includes a method to assess the seismic incident hazard, defined as an event whose intensity could cause nuisances for the population, affect the local buildings and infrastructures and which could adversely impact the operating conditions and even the continuation of the project. This method has been developed based on more than 50 case studies consisting of projects representative of different types of geothermal systems where induced seismicity occurred or not. Based on an iterative approach, the method recommends hazard assessment at each key step of a geothermal project to benefit from the additional knowledge it bring. The main key steps identified are the initial assessment before frilling occur, a reevaluation just after drilling and a reevaluation before any potential stimulations. Hazard assessment is based on a decision tree approach, involving specific criteria for each project phase. The seismic incident hazard is rated with a score between 0 and 3. At the lowest level (0), no specific measures to manage induced seismicity are required. For level 1 and 2, monitoring and management methods must be developed. At level 3, the project is considered to be beyond potential induced seismicity management (it’s deviating from the plan) and operations must be suspended pending the outcome of further investigations. This method has been tailored for helping and guiding operators and French administration to consider and manage induced seismicity hazard for every deep geothermal project. 
Stratification in non-tidal shallow coastal lagoons during extreme summer heatwaves 
Authors: Reese Lloyd , Gräwe Ulf , Lange Xaver , Burchard Hans
Published: March 2024

Due to their shallow depth, coastal lagoons are often considered to be vertically well-mixed. However, past studies have shown that, depending on forcing conditions, vertical density stratification may in fact occur even in lagoons of only a few meters depth. Further, many coastal lagoons are faced with a multitude of anthropogenically caused pressures, including eutrophication as well as a likely increasing occurrence of summer heatwaves and other extreme atmospheric conditions due to climate change. While eutrophication leads to increased biological productivity and a subsequently increased oxygen demand, high water temperatures during heatwaves lead to reduced oxygen solubility, thus aggravating the risk of anoxic conditions within the water body. As vertical stratification acts to suppress vertical mixing, it may facilitate the occurrence of bottom oxygen depletion in such waters. Since coastal lagoons are of great ecological and economical interest due to their multiple ecosystem services, e.g., as spawning grounds for fish, it is of utmost importance to assess the conditions under which vertical stratification may occur. Only with such knowledge it will be possible to estimate the future development of coastal lagoon ecosystems. While many past studies have covered stratification of freshwater lakes during heatwaves, there is a significant gap of research covering coastal lagoons under the same conditions, where an additional forcing is added via the connection to the open sea. In particular, non-tidal, non-choked lagoons are currently understudied with respect to summer heatwaves. In our study, we therefore aim to assess the conditions under which stratification may occur in such lagoons during a mid-latitude summer heatwave. To this end, we have applied a non-dimensional parameter space analysis to a numerical, one-dimensional water column simulation of such a lagoon. Here, we present first results from this analysis.
Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring
Authors: Yuan Zhendong , Kerckhoffs Jules , Li Hao , Hoek Gerard , Vermeulen Roel
Published: March 2024

Many epidemiological studies have traditionally leveraged European maps derived from fixed-site measurements to investigate health effects, primarily emphasizing inter-city variations. Recently, mobile monitoring has been demonstrated to refine the spatial resolution focusing on intra-city variations. Nevertheless, efficiently scaling mobile monitoring campaigns to cover a large spatial area remains challenging.Tackling this challenge, we explored the transferability of mobile measurements across three European cities. We propose to adapt the traditional land use regression (LUR) models with unsupervised transfer learning algorithms. These models, named CORrelation ALignment (Coral) and its adapted form, inverse distance-weighted Coral (IDW_Coral), aim to estimate air pollution levels in Amsterdam. They rely solely on data from mobile monitoring campaigns in Copenhagen and Rotterdam, bypassing the need for local Amsterdam data itself. The first 30 collection days of mobile campaigns in Copenhagen and Rotterdam were used as the source data (training inputs). By harmonizing the feature space, Coral is designed to minimize the domain difference between the source and target areas. IDW_Coral integrates single Coral models following general geographic principles. Their performance was validated against external routine measurements and compared with a reference LUR model (AMS_SLR), fitted by sequentially increasing amounts of mobile measurements collected in Amsterdam for nitrogen dioxide (NO2). The proposed models were also compared with our previously published mixed-effect models using all Amsterdam mobile measurements for NO2 and Ultra Fine Particles (UFP).For nitrogen dioxide (NO2), IDW_Coral achieved a balanced performance with an R2 of 0.35.  This accounts for 67% of the accuracy of a locally fitted Amsterdam model (AMS_SLR, R2 = 0.52), developed using comprehensive mobile monitoring over 160 days in Amsterdam. The difference in absolute errors between the two models was marginal (0.75 for MAE and 0.66 µg/m3 for RMSE). The R2 of IDW_Coral matches that of AMS_SLR based on 25 days of data collection, implying that a minimum of 25 days is required to gather city-specific insights through mobile monitoring. If this condition isn't met, IDW_Coral presents a more cost-effective alternative. IDW_Coral correlated strongly (Spearman correlation of 0.72 for NO2 and 0.76 for UFP) with mixed-effect models fitted with all Amsterdam mobile measurements.Leveraging Tobler's first law of Geography, our IDW_Coral method proficiently delineates hyperlocal air pollution in areas not directly observed. Further improvements in accuracy and applicability can be achieved by expanding mobile-monitored areas. Requiring no direct measurements in the target area, IDW_Coral has the potential for application across Europe, promising substantial savings in collection efforts.
Investigating radical processes at the surface of secondary organic aerosols  
Authors: McConnell Abigail , Stone Daniel , Heard Dwayne
Published: March 2024

Secondary Organic Aerosols (SOAs) have been estimated to be the highest proportion by mass of atmospheric aerosols averaged globally and a significant fraction of particulate matter below 2.5 μm (PM2.5). Previous studies have established its formation pathways, but fewer studies have focused on the processing of SOAs and how SOAs interact with trace gas species in the atmosphere. Concentrations of HO2, a critical radical in many atmospheric processes, are often overestimated in atmospheric models. These discrepancies have sometimes been attributed to the heterogeneous uptake onto atmospheric aerosols. There is a significant lack of data with respect to the uptake of HO2 onto secondary organic aerosols. The principal objective of this project is to explore the heterogeneous reactions of HO2 occurring on the surface of atmospherically relevant secondary organic aerosols.Atmospherically relevant SOAs have been produced in a Potential Aerosol Mass Chamber (PAM) from the oxidation with OH and ozone of volatile organic compounds, α-pinene, d-limonene and 1,3,5 – trimethyl benzene. A scanning mobility particle sizer (SMPS) characterised the aerosol's physical properties. Results from these chamber studies show that the size distribution of the SOA can be altered by changing the initial mixing ratio of the VOC or oxidant. A flow tube coupled to a Fluorescence Assay Gas Expansion (FAGE) detection cell, which utilises laser-induced fluorescence (LIF) spectroscopy, is used to measure radical species in the gas phase.HO2 uptake is observed by an increased loss of HO2 with increasing aerosol surface area. There is competition between the uptake of HO2 onto SOAs and the production of HO2 from SOAs. Thus, both processes must be well understood to obtain an HO2 uptake coefficient for SOAs and are investigated in this presentation.
Past and future changes of streamflow in the European Alps
Authors: Guo Rui , Nguyen Hung , Galelli Stefano , Ceola Serena , Montanari Alberto
Published: March 2024

Short instrumental streamflow records in the European Alps limit our understanding of the full range and long-term variability in river discharge, which could greatly impact the management of freshwater resources for the densely populated area downstream. Enhancing the understanding of past climatological and hydrological information is also essential to improve the accuracy of future scenarios for rare extreme events, such as multi-year droughts and unprecedented floods, which recently impacted severely important water resource systems and communities at the global level. Tree-ring data have proven to be a viable opportunity for reconstructing various climatic parameters, including streamflow. By using a novel climate-informed framework, the station-based streamflow records of several rivers originating from the European Alps are reconstructed dating back to the year 1100 AD. To further investigate the characteristics of streamflow and extreme events in both the past and future, this study also relies on state-of-the-art paleo simulations from the Paleoclimate Modeling Intercomparison Project phase 4 (PMIP4) and future projections from Coupled Model Intercomparison Project phase 6 (CMIP6). By integrating proxy-based reconstructions, climate model simulations and projections, and observation, the changes in streamflow and rare extreme events in the European Alps are put into a longer perspective covering both the past nine centuries and one century into the future, thus providing a unique opportunity to assess the risk of extreme events and to inform more effective water management strategies for climate change adaptation.
Decadal Variability of the Indonesian Throughflow’s Vertical Structure and the Impact on Heat and Salinity Transport
Authors: Waitzmann Daniel , Wang Shouyi , Oppo Delia W. , Ummenhofer Caroline C.
Published: March 2024

The Indonesian Throughflow, a low-latitude passage of the global conveyor belt, transfers water from the tropical Pacific to the Indian Ocean, modulating the properties of both oceans. Observational and modelling studies have shown that the interannual and decadal variability of the Indonesian Throughflow is closely linked to the leading climate modes of the tropical Pacific, namely the El Niño Southern Oscillation and the Interdecadal Pacific Oscillation; further, it is modulated by variability in the Indian Ocean, especially in the outflow region. The Indonesian Throughflow volume transport variability affects salinity and temperature transport and ocean-atmosphere exchange in the Indo-Pacific warm pool. The Makassar Strait transport represents about 80% of the total Indonesian Throughflow transport and is, therefore, a good proxy for the Indonesian Throughflow transport. Observations from the Indonesian Seas have been used to explain the variability on seasonal to interannual time scales. However, due to the lack of long observational time series in the region, assessing the variability and driving mechanisms on longer time scales is challenging. Here, we use transient runs of a high-resolution coupled ocean-atmosphere model to address the decadal variability of the Indonesian Throughflow and its change under global warming over the time period 1850-2102. We assess how heat content, salinity, and volume transport in the Makassar Strait region change on these timescales and how they contribute to the heat and freshwater transport changes. In addition, we investigate the vertical structure of the Indonesian Throughflow variability and its driving mechanisms. This involves understanding how Indonesian Throughflow variability is connected more broadly to large-scale conditions in the Pacific and Indian Oceans. The results presented here may motivate further analysis using multiple simulations of the high-resolution model configurations conducted as part of HighResMIP to assess the forced changes to the Indonesian Throughflow under RCP8.5 forcing in a highly dynamic ocean region that plays a pivotal role in global heat and freshwater transport.
Annual Cycle of Antarctic Sea Ice Deformation from ICESat-2
Authors: Duncan Kyle , Farrell Sinead
Published: March 2024

Since 2015, Antarctic sea ice extent has declined by ~53%, and reached consecutive record lows in the austral summers of 2022 and 2023. These events have raised major concern as to whether a regime shift towards more extreme and frequent low sea ice extents has begun. Potential impacts on the climate system are far-reaching and continent-wide studies are urgently needed. Due to Antarctica's remote and harsh environment, in situ observations are however sparse. NASA's ICESat-2, a laser altimeter launched in 2018, provides precise sea ice surface elevation data at high-resolution, with along-track sampling every ~0.7 m. This sampling allows us to resolve meter-scale features in the sea ice pack, such as pressure ridges, which modify the shape of the ice thickness distribution and play an important role in ocean-atmosphere momentum flux through form drag. We apply a bespoke processing technique, called the University of Maryland-Ridge Detection Algorithm (UMD-RDA), to derive the surface topography of sea ice in the Southern Ocean over a 5-year period, spanning 2018-2023. Using the UMD-RDA we can capture seasonal and interannual variability in surface roughness, pressure ridge sail height, and ridge sail spacing. We find that during the 2018-2023 period, on average, across the full Southern Ocean ice pack, sea ice surface roughness reaches a maximum in January/February followed by a minimum in April. Sail height is at its maximum in January and minimum in June, while sail spacing is at its minimum in January and maximum in June. Interannual variability shows that the 2023 season is an outlier with respect to the 5-year 2018-2023 average. In 2023 there was a decrease of ~20% in surface roughness during the April minimum, a decrease of ~8% in sail height during the June minimum, and an increase of ~31% in sail spacing during the June maximum. This suggests that, in 2023, the sea ice pack was less deformed overall than in preceding years. We also assess seasonal and interannual variability in surface roughness and ridge morphology in five distinct regions including the Amundsen-Bellingshausen (A-B), Ross, Pacific, Indian, and Weddell Seas. The A-B, Ross, and Pacific sectors showed the greatest change in 2023, with respect to the 2018-2023 average, with a decrease in surface roughness of ~34%, ~26%, and ~14%, respectively. Sail spacing within the A-B, Ross, and Pacific sectors, with respect to the 2018-2023 average, increased by ~83%, ~61%, and ~35%, respectively. Furthermore, the ICESat-2 ATL10 sea ice freeboard dataset shows a ~50% decrease in freeboard within the A-B sector in 2023. These results provide evidence that a substantial amount of thicker, older, and rougher ice was likely exported out of the A-B region. Our results can provide insight into the mechanisms responsible for the recent record low sea ice extents and could uncover new relationships between deformation, roughness, and ice extent.
The influence of belowground nematode herbivory on carbon allocation in drought-prone ecosystems
Authors: Franco André , Gherardi Laureano
Published: March 2024

To understand carbon dynamics and how it is affected by ongoing climate change, we need a better appreciation of the belowground ecological interactions driving plant allocation patterns and ecosystem carbon fixation. It has become increasingly clear that belowground root inputs contribute significantly more to soil carbon sequestration than aboveground plant inputs. Yet, current understanding of the role of belowground root herbivory in ecosystem carbon dynamics is weaker than that of aboveground herbivory. We addressed this gap by merging three complementary and novel areas of research, namely testing how: (1) biotic interactions between plants and nematode herbivores affect belowground biomass allocation in grasses; (2) how these biotic interactions and their consequences for biomass allocation are modified by a pervasive perturbation, namely drought, which is becoming more intense and frequent; and (3) how belowground responses vary across contrasting ecosystems. Results of complementary controlled and multi-site field experiments showed that: (1) nematode root herbivory modulates the relationship between water availability and belowground biomass allocation; (2) drought-induced increases in nematode root herbivory impede plants from increasing biomass allocation to roots under drought; and (3) these nematode effects are greater in magnitude in mesic compared to semiarid and arid grasslands. These findings suggest that the fate of carbon in mesic ecosystems under increasing drought frequency is highly influenced by nematode herbivores in the soil, and encourage investigations into the unknown consequences for soil carbon formation and persistence.
FEEDS: Validation of the Framework for Evaluation of Early Detection Systems
Authors: Cannavo Flavio , Minio Vittorio , Privitera Eugenio , Di Lieto Bellina , Romano Pierdomenico , Innocenti Lorenzo , Lacanna Giorgio , Ripepe Maurizio
Published: March 2024

Monitoring volcanic activity is a complex task, given the intricate nature of volcanic processes and the diverse eruptive styles exhibited by different volcanoes. Early Detection (ED) systems have emerged as indispensable tools for mitigating potential risks associated with volcanic eruptions. The effectiveness of these systems is contingent upon their ability to provide timely and accurate alerts, as false alarms or missed warnings can lead to economic repercussions and pose risks to infrastructure and human safety. Evaluating the reliability of the ED systems may be paramount not only for effective hazard mitigation but also for facilitating the implementation and optimization of an ED model. However, developing an ED model is a challenging and labor-intensive endeavor, also requiring a deep understanding of advanced techniques and a meticulous calibration of various parameters. In response to these challenges, we present the Framework for Evaluation of Early Detection Systems (FEEDS). FEEDS is a comprehensive Python-based package designed to automatically assess the generalization capability of generic ED systems through cross-validation. The framework introduces a generic class representing the ED model identified solely through data, enabling a systematic assessment based on essential predictive parameters, including True Positive Rate, False Discovery Rate, prediction time, and Fraction of Time in Alarm, by performing a simulation. To validate the effectiveness of FEEDS, we utilized tiltmeter and strainmeter data recorded at Stromboli volcano between 2007 and 2019. These datasets, managed by Istituto Nazionale di Geofisica e Vulcanologia and Università di Firenze, were employed to implement FEEDS with a customized model for the early detection of the paroxysmal activity affecting the volcano during the period of the study, demonstrating the practical applicability and reliability of this framework in real-world volcanic monitoring scenarios.FEEDS may represent a valuable contribution to the ongoing efforts to enhance ED systems and their application in mitigating volcanic hazards. The development of a robust framework that automates the standard evaluation process not only streamlines system implementation but also reduces user efforts and establishes a common ground for assessing the reliability and performance of different ED models, contributing significantly to the advancement of volcanic monitoring capabilities.
Lock-ins and path dependency in evaluation metrics used for hydrological models
Authors: Melsen Lieke , Puy Arnald , Saltelli Andrea
Published: March 2024

Science, being conducted by humans, is inherently a social activity. This is evident in the development and acceptance of scientific methods. Science is not only socially shaped, but also driven (and in turn influenced) by technological development: technology can open up new research avenues. At the same time, it has been shown that technology can cause lock-ins and path dependency. A scientific activity driven both by social behavior and technological development is modelling. As such, studying modelling as a socio-technical activity can provide insights both in enculturation processes and in lock-ins and path dependencies. Even more, enculturation can lead to lock-ins. We will demonstrate this for the Nash-Sutcliffe Efficiency (NSE), a popular evaluation metric in hydrological research. Through a bibliometric analysis we show that the NSE is part of hydrological research culture and does not appear in adjacent research fields. Through a historical analysis we demonstrate the path dependency that has developed with the popularity of the NSE. Finally, through exploring the faith of alternative measures, we show the lock-in effect of the use of the NSE. As such, we confirm that the evaluation of models needs to take into account cultural embeddedness. This is relevant because peers' acceptance is a powerful legitimization argument to trust the model and/or model results, including for policy relevant applications. Culturally determined bias needs to be assessed for its potential consequences in the discipline. 
Local climate influences δO2/N2 variability in ice core records
Authors: Harris Stuart Romilly , Landais Amaëlle , Arnaud Laurent , Buizert Christo , Capron Emilie , Dumont Marie , Libois Quentin , Mulvaney Robert , Orsi Anaïs , Picard Ghislain , Prié Frédéric , Severinghaus Jeffrey , Stenni Barbara , Martinerie Patricia
Published: March 2024

Orbital dating using δO2/N2 records is a powerful tool for constructing ice core chronologies in deep ice cores due a widely observed anti-correlation with summer solstice insolation (SSI). While understood to be linked to near-surface snow metamorphism, the physical mechanisms driving this process remain poorly constrained and the role of local accumulation rate and temperature have been scarcely considered. We primarily present the results of our new study which uses a compilation of records from 14 ice cores to show a significant dependence of mean δO2/N2 on local accumulation rate and temperature. Using EPICA Dome C as a case study, we then show that during rapid climatic changes, an accumulation/temperature signal may be superimposed on top of the SSI signal and therefore should be accounted for when using peak-matching techniques for future dating of deep ice cores, such as the EPICA or Beyond EPICA cores.Further to our study, we include new δO2/N2data measured in shallow, bubbly ice just below close-off from two newly drilled firn cores at sites with distinct close-off conditions; D-47 and Little Dome C (the Beyond EPICA site), which support our findings. Moreover, thanks to parallel firn air pumping campaigns, overlapping data from open and closed porosity at these two sites promise greater insight into the mechanisms driving close-off fractionation.
Flash Flood Prediction with Neural Networks using Ensemble Methods to address Input and Model Uncertainties
Authors: Reinecke Arne , Neuweiler Insa , Steinbrich Andreas , Leistert Hannes , Hänsler Andreas , Weiler Markus , Brendt Thomas , Huth Bettina
Published: March 2024

Short-term flood and inundation forecasts, especially for spatially limited, convective heavy rainfall events, are challenging due to the very short lead time of heavy rainfall predictions. They are subject to different uncertainties. Due to the short forecasting period, data-driven machine learning methods have been developed to predict the inundated areas in almost real time. The time-consuming numerical, hydrodynamic simulation of flooding depths and flow velocities is replaced by a surrogate model.We use a neural network as surrogate model, which is trained with a large ensemble of hydrodynamically simulated water levels and flow velocities for a specific catchment using an ensemble of spatial and temporal surface runoff. The surface runoff is calculated by the hydrological model RoGeR at a 5 m resolution and spatial and temporal varying heavy rainfall events as input. The neural networks are able to predict spatially resolved maximum water depths, maximum discharges and maximum flow velocities for an event in the specific catchment area of 25km² in significantly less than one second.The fast prediction time allows to consider uncertainties in the forecast. Uncertainties in flood forecasts typically result mainly from uncertain precipitation input (or forecast), initial conditions (e.g. soil moisture), lack of data of relevant parameters or their limited transferability in space. Although the model error of the hydrological and hydraulic model itself, such as assumptions about geometry and parameters or underlying flow equations, is an important source of uncertainty, we address in this presentation only the uncertainties of input and initial conditions.To generate ensemble calculations that represent the probability distribution of predicted flood height and velocity, we create a large ensemble of input variables by statistically varying the initial soil moisture as well as the location and intensity of the precipitation fields. Since the trained neural network topology is another source of uncertainty, differently trained networks with different network topologies are also considered in the ensembles.Using the ensembles, we can specify prediction intervals for spatially resolved maximum water levels, maximum discharge and flow velocities, which result from the uncertainty of the model input, model parameters and the inaccuracy of the surrogate model. Using the ensemble approach, we discuss the impact of the different sources of uncertainty on the predictions.As an example, it is found that the statistical variation of the meteorological input data has a greater influence on the prediction interval width than the statistical variation of the initial soil moisture. In general, it can be concluded that sufficient variability in the training data needs to be covered to make reasonable uncertainty predictions.
Reduction of temporal variations in tidal parameters by application of the local response models at globally distributed SG stations
Authors: Ciesielski Adam , Forbriger Thomas , Zürn Walter , Rietbrock Andreas , Dykowski Przemysław
Published: March 2024

The already 100 years old harmonic analysis of tides is based on the assumption of separable and non-separable contributions depending on the time series length (Rayleigh criterion in tidal analysis). A priori wave groups had to be composed of different harmonics, which leads to an inaccurate (biased) estimate of tidal parameters. An alternative Regularization Approach to Tidal Analysis, RATA, constrains the solution to be close to a reference model what stabilises the linear regression, making wave grouping obsolete. In this way, the resolution power of the harmonic analysis is exploited to a much larger extent, since the risk of over-fitting is strongly reduced.We used RATA method to analyse data from globally distributed superconducting gravimeters (SGs) and we are able to achieve super resolution that even highly violates the Rayleigh criterion. The results from double-sphere SG instruments give an indication of the minimum error for the accuracy. We estimated local response models for over 10 stations in Europe, which confirms the consistency of the method. The small differences in phases and amplitudes are most likely caused by ocean loading with varying distance to the ocean. The investigation of stations on other continents reveals significant disparities between the observed tidal response (which accounts for the loading signals as well) and the Earth body model assumptions (like Wahr-Dehant-Zschau elastic analysis model).Temporal variations of tidal parameters, seen in the moving window analysis (MWA), are known for all tidal wave groups at different SG stations around the globe. The amplitude of variations usually is greater than the standard deviation by a factor of 2 (minimum) to 32 (maximum). In our investigation, we approximated the effect of the time-invariant ocean loading and radiation tides in the data by application of the local response models, already estimated with RATA. We repeated the MWA of 12 wave groups composed from summed harmonics. We found that the periodic variations of groups M2, K1, µ2, N2, L2, and S2 are reduced by up to a factor of 9 compared to earlier studies. Some long-period variations previously seen in the M1, O1, Q1, and J1 groups are captured as well. The previously neglected influence of radiation tides, degree 3 tides, and significant satellite constituents were the main causes of apparent modulations in previous studies. Hence, with the local model correction, a proper investigation of the remaining temporal variations to study instrument stability or time-varying contributions of ocean loading is more applicable.
Exploring multiyear-to-decadal North Atlantic sea level predictability using machine learning and analog methods
Authors: Gu Qinxue , Jia Liwei , Zhang Liping , Delworth Thomas , Yang Xiaosong , Zeng Fanrong , Li Shouwei
Published: March 2024

Long-term sea level rise and multiyear-to-decadal sea level variations pose substantial risks of flooding and erosion in coastal communities. The North Atlantic Ocean and the U.S. East Coast are hotspots for sea level changes under current and future climates. Here, we employ a machine learning technique, a self-organizing map (SOM)-based framework, to systematically characterize the North Atlantic sea level variability, assess sea level predictability, and generate sea level predictions on multiyear-to-decadal timescales. Specifically, we classify 5000-year North Atlantic sea level anomalies from the Seamless System for Prediction and EArth System Research (SPEAR) model control simulations into generalized patterns using SOM. Preferred transitions among these patterns are further identified, revealing long-term predictability on multiyear-to-decadal timescales related to shifts in Atlantic meridional overturning circulation (AMOC) phases. By combining the SOM framework with “analog” techniques based on the simulations and observational/reanalysis data, we demonstrate prediction skill of large-scale sea level patterns comparable to that from initialized hindcasts. Moreover, additional source of short-term predictability is identified after the exclusion of low-frequency AMOC signals, which arises from the wind-driven North Atlantic tripole mode triggered by the North Atlantic Oscillation. This study highlights the potential of machine learning methods to assess sources of predictability and to enable efficient, long-term climate prediction.
Evaluating the impact of using Δ14CO2 observations in quantifying fossil CO2 emissions over Europe
Authors: Gómez-Ortiz Carlos , Monteil Guillaume , Karstens Ute , Basu Sourish , Scholze Marko
Published: March 2024

CO2 is one of the most important greenhouse gases responsible for global climate change. Today’s atmospheric CO2 concentration has risen by nearly 49% compared to pre-industrial times, mainly caused by fossil fuel combustion and cement production. CO2 emission reports under the Paris Agreement are crucial in understanding the main sources responsible for these emissions, and spatial and temporal distributed emission inventories such as EDGAR and ODIAC have been important tools to gain additional insights about when and where these emissions happen. However, increasing the resolution comes with an increment in the uncertainty at sub-annual and sub-national scales. One way to improve the knowledge on these emission inventories is by inverting in situ observations of CO2 and Δ14CO2 at regional scales. Radiocarbon (14C) is the radioactive isotope of carbon, and due to its half-life time of ~5730 years, it is not present in fossil fuels, making it a good tracer for the natural carbon cycle.In this study, we evaluate the impact of incorporating Δ14CO2 observations from the Integrated Carbon Observation System (ICOS) network and its sampling strategy over Europe into the Lund University Modular Inverse Algorithm (LUMIA) for optimizing fossil CO2 and the biosphere fluxes in a horizontal grid of 0.5° x 0.5° and on a weekly temporal resolution. Δ14CO2 is currently mostly measured in 2-weekly integrated samples. As part of the EU-funded CORSO (CO2MVS Research on Supplementary Observations) Project, an intensive campaign collecting 1-hour flask samples every third day at 10 Western/Central European sampling stations will be performed during 2024. W perform a series of Observing System Simulation Experiments (OSSEs) using various model products (EDGAR/ODIAC, LPJ-GUESS/VPRM) as prior fluxes and as assumed true fluxes for fossil and biosphere fluxes to generate a time series of synthetic observations. We first demonstrate the impact of adding Δ14CO2 observations in addition to the CO2 observations to recover the true fossil and biosphere flux time series and the assumed true total CO2 annual budget over Europe. Further, we assess the impact of the sampling strategy by comparing a simulation using only Δ14CO2 integrated samples against a simulation including the CORSO sampling strategy. In the latter case, we find a notable improvement in recovering the fossil CO2 emissions in Western/Central Europe and countries such as Germany and France. We also evaluate other sampling strategies, such as selecting observations with the largest apportionment of fossil CO2 and the lowest impact of nuclear emissions. Such approaches seem to be a promising way to improve the quantification of fossil CO2 emissions in regions with a dense sampling network and neighbouring regions such as Eastern Europe.