bluuebunny/crossref_metadata_2025 huggingface dataset:
Leveraging data challenges to enhance Copernicus Marine Service products and align with the future European Digital Twin of the Ocean.
Authors: Metref Sammy , Ballarotta Maxime , Ubelmann Clément , Beauchamp Maxime , Busché Clément
Published: March 2024

The SLICING (Sea Level Innovations and Collaborative Intercomparison for the Next-Generation products) project is a Copernicus Service Evolution project that responds to the evolving landscape of sea level processing. SLICING promotes a novel approach centered on open and collaborative data challenges for altimetric product developments and assessments. With a focus on fostering collaboration and methodological advancement, SLICING aligns with the objectives of the Copernicus Marine Service (CMEMS) and the overarching spirit of the European Digital Twin of the Ocean (DTO). With the growing influence of artificial intelligence in oceanography and the advances of oceanographic observations, such as the new SWOT satellite, the surge in innovative methodologies and processing techniques is huge. However, these scattered efforts and the lack of generic and open comparisons is still a hindrance to reliably inform operational process chains. SLICING proposes a structured framework for the comparison of state-of-the-art methods and operational products to assess processing techniques and improve altimetry products. This framework not only aims to enhance the quality of observation and mapping processing for CMEMS but also provides a blueprint for the CMEMS Sea Level Thematic Assembly Centre evolution. By formulating altimetric processing problems as data challenges on collaborative platforms, SLICING attempts to provide a link between scientific innovation and operational implementation, addressing the gap between research and production. These challenges, rooted in rigorous software development practices, transcend mere method testing. They act as catalysts for collaboration, bringing together scientists around key operational altimetric processing issues. In this presentation, we show examples of open comparisons for preprocessing the new SWOT data and mapping conventional altimetry. Through these examples, we offer a comprehensive overview of the strides made during the SLICING project, shedding light on advancements in sea level processing and the facilitation of collaborative data challenges. This overview can serve as a valuable exploration into the dos and don'ts that emerged from the project, offering insights into effective strategies for fostering collaborations and enhancing the quality of oceanographic products. By distilling the lessons learned, this broader perspective is intended to guide future initiatives, both within the CMEMS and in the broader context of the European DTO. Indeed, through shared problem formulation, evaluation metrics, and reproducible practices, the work carried out in the SLICING project exemplifies how the spirit of collaborative data challenges aligns with the DTO's vision of a unified digital platform. 
Rapid and Accurate Tsunami Forecast Method Using Tsunami Data Assimilation with Real-time Source Estimation.
Authors: Tanioka Yuichiro , Atobe Yota
Published: March 2024

 After the dense observation network for earthquakes and tsunamis along the Japan trench (S-net) was available, the tsunami data assimilation became a powerful technique to compute tsunamis in real-time. However, a problem exists for this technique to forecast tsunamis in real-time because the observation data near the source area are always unstable as shown in the 2016 Fukushima earthquake. Therefore, the data near the source area may not be available for real-time tsunami forecast. In this paper, we try to solve this problem by combining the tsunami data assimilation with the source estimation using real-time GNSS observation such as REGARD. We tested our method for two cases, the 2016 Fukushima earthquake case and the 1896 Sanriku earthquake case. We first computed the tsunamis from the source models estimated by Kubota et al. (2021) for the 2016 Fukushima case and that estimated by Satake et al. (2017) for the 1896 Sanriku earthquake case as reference tsunamis. We used those commuted data at stations of S-net as observation data without the stations near the source area. Then the tsunami data assimilations with and without the rectangular fault model are performed. The result of the 2016 Fukushima case shows that the tsunami data assimilation worked well although the quickly estimated rectangular fault model using the GNSS observation data was not acceptable for the tsunami simulation. The result of the 1896 Sanriku case shows that the tsunami data assimilation with the estimated rectangular fault forecast the acceptable tsunami waveforms along the coast about 20 minutes faster that that without the rectangular fault. This improvement is significant, so our method can be used as a real-time tsunami forecast technique even the tsunami data near the source area will not be available.  ReferencesKubota, et al. (2021). Improving the constraint on the Mw 7.1 2016 off-fukushima shallow normal-faulting earthquake with the high azimuthal coverage tsunami data from the s-net wide and dense network: Implication for the stress regime in the tohoku overriding plate. Journal of Geophysical Research: Solid Earth, 126(10), 79. https://doi.org/10.1029/2021jb022223Satake, K., Fujii, Y. & Yamaki, S. (2017) Different depths of near-trench slips of the 1896 Sanriku and 2011 Tohoku earthquakes. Geosci. Lett. 4, 33. https://doi.org/10.1186/s40562-017-0099-y
A feature-based framework to investigate atmospheric predictability.
Authors: Schmidt Sören , Riemer Michael , Selz Tobias
Published: March 2024

Atmospheric predictability is intrinsically limited by the upscale growth of initial small-scale, small-amplitude errors. For practical predictability, model error and initial-condition uncertainty also contribute significantly. The accurate representation and interactions of these factors within numerical weather prediction systems determine the extent to which forecast uncertainty is correctly modeled. An improved understanding of upscale error-growth mechanisms and their flow dependence in numerical weather prediction models has several implications: it enables more focused model verification and development, aids in recognizing limitations in emerging forecasts systems like machine-learning-based approaches, and may indicate when the intrinsic limit of predictability has been reached. Studying the flow dependence of error growth requires a local perspective, which is not provided by the traditional spectral perspective on upscale error growth. We here take a complementary approach and apply a feature-based perspective. We have developed an automated algorithm to identify error features in gridded data and track their spatial and temporal evolution. Errors are considered in terms of potential vorticity (PV) and near the tropopause, where they maximize. A previously derived PV-error tendency equation is evaluated to quantify the different contributions to error-growth experiments with the global prediction Model ICON from the German Weather Service. Errors in these experiments grow from differences in the seeding of a stochastic convection scheme. In a suite of experiments, this source of uncertainty competes with initial-condition uncertainty of varying magnitude. Evaluation of the process-specific error-growth rates allow the detailed quantification of the upscale-growth mechanisms. For this purpose, we integrate the growth rates over the respective area associated with an error feature. Examination of the combined growth rates of all features in an upscale-error-growth experiment reproduces a previously found three-stage multi-scale upscale-growth paradigm. Illustration the importance of flow dependence, the growth rates from a single feature can substantially differ from the overall average. Further highlighting this importance, intrinsic limits of predictivity can be identified for some features even in the presence of substantial initial-condition uncertainty. The presentation will conclude with a comparison of error evolution in conventional numerical weather prediction systems to a data-driven, machine-learned model.
December 2021 Jökulhlaup impact on landform and sedimentary assemblages on the decoupled Skeiðarársandur system, SE Iceland: implications for the Quaternary record.
Authors: Callard Louise , Pitman Sebastian , Harrison Devin , McDonald Neil , Perks Matthew , Bainbridge Rupert , Roberts Matthew , Snell Jenny , Russell Andrew
Published: March 2024

Since the turn of the 21st century the appearance and expansion of the most recent proglacial lakes fronting Skeiðarárjökull in SE Iceland, has led to the sandur being disconnected or decoupled from the glacier. Consequently, the sediment that would otherwise be deposited on the sandur is instead trapped within these lakes, leading to sediment deprivation of the distal sandar which in-turn impacts the fluvial and coastal systems. The recent formation of proglacial lakes also provides new challenges for jökulhlaup hazard assessment. Despite their importance, there have been no detailed studies of this large-scale proglacial sedimentary systems undergoing active decoupling, and the role of this process for sediment flux and landscape development remains unclear. In December 2021, Grimsvötn subglacial lake drained 0.9 km3 of water as a jökulhlaup from Skeiðarárjökull. A comprehensive survey of the proglacial lakes (sub-bottom profiling and single beam echosounder) and proximal sandur system (ground penetrating radar (GPR) and UAS survey), along with the collection of sediment cores, was conducted after the event. This provides a rare opportunity to capture the geomorphological and sedimentary signature of a jökulhlaup within a subaqueous setting and the downstream fluvial system. We present a model of the controls on jökulhlaup impact on landform and sedimentary assemblages within the proglacial lakes and connected glacifluvial system of Skeiðarársandur. This provides a modern analogue for Quaternary glacier and ice sheet margins.
Nitrous oxide dynamics in coastal marine surface sediments
Authors: van Erk Marit R. , Arévalo-Martínez Damian L. , Jetten Mike S.M. , Slomp Caroline P.
Published: March 2024

Coastal seas are especially vulnerable for eutrophication and deoxygenation. These processes can lead to major changes in oxygen concentrations within both coastal bottom waters and surface sediments. The dynamics of the greenhouse gas nitrous oxide (N2O) are strongly coupled to oxygen dynamics, and changes in oxygenation can thus lead to changes in N2O production and consumption patterns. Deoxygenation therefore has the potential to affect the exchange of N2O between surface sediments and bottom waters, which in turn might affect the water column budget of N2O and eventually its exchange across the sea-air interface. Here, we present depth profiles of oxygen and N2O as determined with microsensors within coastal marine surface sediments from Lake Grevelingen (The Netherlands) in March 2023. Lake Grevelingen is a eutrophic coastal system suffering from seasonal bottom water anoxia and euxinia. We present results for several field locations and results of laboratory experiments in which oxygen concentrations were manipulated. On-board measurements revealed high concentrations of N2O in the top sediments (~1 mm), where oxygen was also present. We find that increased depths of oxygen penetration in the sediment lead to higher penetration depths of N2O. We discuss the potential drivers of N2O fluxes between the surface sediments and bottom waters under different oxygenation conditions.         
Patterns of extreme high flows in relation to their dominant generating processes across Sweden
Authors: Hundecha Yeshewatesfa , Olsson Jonas , Simonsson Lennart , Rosberg Jörgen
Published: March 2024

Understanding the nature of flooding of a region is key for flood management. The impact of flooding depends on how spatially extensive it is and this, in turn, is influenced by the processes generating the flood. In this study, we investigated the relative importance of rainfall and snowmelt in the generation of floods of different magnitudes and characterized their spatial patterns in different climate regions of Sweden. We generated a large number of spatially diverse extreme river flow scenarios across Sweden that are statistically consistent with the observations by employing a multi-site weather generator and a highly resolved semi-distributed hydrological model. The extreme flows within each of the main rivers were classified based on their generating meteorological forcing and the spatial distribution of the flow magnitudes was assessed. The results reveal that rainfall is the main contributor of extreme flows of all magnitudes in the southern part followed by rain-on-snow, while in the northern part, rain-on-snow is the main process resulting in extreme flows followed by rainfall. Pure snowmelt is the least contributor of extremes in all regions and its contribution decreases with increasing magnitude of the flow. The proportion of events generated by rainfall increases with the magnitude of the flow in all regions. Extremes of lower magnitudes are generally more spatially widespread than the higher extremes and events generated by snowmelt and rain-on-snow are spatially more widespread than events generated by rainfall.The possible impact of climate change was also assessed by generating extreme flows for end-of-century climate change scenarios by perturbing the weather inputs generated by the weather generator using data from a set of regional climate models and using them to force the hydrologic model. The results show that the main generating processes in each region remain the same. However, the proportion of rainfall generated events will be markedly higher than under the present climate. 
Hydrothermal processes at the Pompeii hydrothermal field: insights from the association of a large sulfide deposit and talc-rich hydrothermal mounds (Mid-Atlantic Ridge) 
Authors: Pelleter Ewan-Loiz , Cathalot Cecile , Rospabe Mathieu , Giunta Thomas , Dupre Stephanie , Maia Marcia , Boissier Audrey , Cheron Sandrine , Rovere Mickaël , Germain Yoan , Guyader Vivier , Donval Jean-Pierre
Published: March 2024

The Pompeii hydrothermal field was discovered in July 2022 during the HERMINE2 cruise [1]. It is located on an inside corner high (21°20 N) at the northern end of a “doomed” ridge segment [2] located just south of the TAMMAR propagating rift. The inside corner high is a domal bathymetric high with gentle slope ridgewards and spreading-parallel lineations (corrugations) characteristic of an oceanic core complex (OCC). The OCC is dissected by several faults including a ridge-perpedicular fault and a series of smaller ridge-parallel faults.The main Pompeii hydrothermal site is located on the corrugated surface atop a spreading-perpendicular rubble ridge. The mound is about 150 m in diameter and 30-40 m high and mainly composed of sulfide-bearing rocks partly covered by Fe-Mn hydrothermal crusts. Sulfide-bearing mineralization mainly consist of quartz and pyrite and are characterized by low copper and zinc concentrations (i.e.
Characterisation and modelling of lightning strikes as point events in time and space
Authors: Zandovskis Uldis , Pigoli Davide , Malamud Bruce D.
Published: March 2024

Lightning, a spatio-temporal phenomenon, comprises of individual strikes with specific occurrence times and spatial coordinates. This study models and characterises lightning strikes from single thunderstorms, treating each strike as a point event. Utilising real-world datasets, we characterise and model lightning strikes' physical properties. Our analysis involves two severe UK thunderstorm systems, selected based on published synoptic analyses. These systems enable subdivision of the lightning dataset into subsets, each representing a distinct thunderstorm. Our two major storm systems feature three thunderstorms each: Storm system A with 7955, 11988, and 5655 strikes over the English Midlands on 28 June 2012; Storm system B with 4218, 455, and 1926 strikes characterised over the northern England on 1-2 July 2015. These six datasets exemplify individual thunderstorms and a total of three physical attributes are : movement speed, lightning inter-event time distribution, and spatial spread about the storm track. Applying least-squares plane and linear fits in the spatio-temporal and lag spaces, we estimate movement speeds of 47-59 km/h and 67-111 km/h for Storm systems A and B, respectively. The inter-event time distribution ranges from 0.01 to 100 seconds, with density peaks around 0.1 seconds and at 1-10 seconds. Autocorrelation analysis in natural time reveals significant autocorrelation in all storms, varying from short-range to long-range. For spatial spread, calculated as the distance from the storm track to the strikes, we employ a linear filter to establish the storm track. This analysis yields typical spatial spreads up to 80 km in either northing or easting dimensions, with an outlier of 226 km in the northing dimension for one storm. The paper concludes with a synthetic lightning strike model. This model allows selection of individual storms' starting points, directions, and movement speeds, generating point events based on our characterisation findings. This comprehensive study of lightning strikes in time and space accurately reflects severe thunderstorms' behaviour and informs statistical models for simulating lightning events.
Hydro-economic assessment of biophysical drought impacts on agriculture
Authors: Nagpal Mansi , Heilemann Jasmin , Klauer Bernd , Gawel Erik , Klassert Christian
Published: March 2024

As climate changes globally and locally, the risk of temperature anomalies, heat waves and droughts have significantly increased. Studies have demonstrated that droughts exert adverse biophysical effects on crop production, posing an unprecedented threat to harvests and resulting in substantial economic losses in Europe. Assessing these biophysical drought impacts on agriculture is crucial for developing effective strategies for drought preparedness, mitigation, and adaptation. This paper contributes to this effort by presenting a framework to estimate economic costs associated with droughts that specifically captures the biophysical impact of climate change on crop output.Existing analyses for drought damages in agriculture are developed for a specific drought event and primarily focus on the reduction in farmer’s income or crop yields in drought events. In these assessments, the biophysical impacts of droughts are not isolated and evaluated from their effects on other economic variables such as output prices, resulting in inaccurate damages. Additionally, lack of single universal definition of drought adds complexity to estimating the costs of droughts. This paper is aimed to contribute by focusing on agricultural droughts, which occurs when variability in soil moisture affects plant growth and development. We simulate this biophysical effect of drought on crop yields by applying a statistical crop yield model to data on soil moisture, temperature and perception. This approach helps isolate the direct impact of drought on agriculture from other changes in aggregate economic production (e.g. business conditions, commodity prices) and farmer management decisions (e.g. intermediate input use). The simulated biophysical yield effects are then quantified into monetary terms to estimate economic damages of droughts. We further look into the relationship of the economic damages and the intensity of droughts to determine drought thresholds that lead to increased economic losses.The results provide bottom-up estimates of the economic damages of drought induced water deficiency in agriculture across Germany for the years 2016-2020. The spatio-temporal patterns of drought impacts can be useful for drought policy planning at local and national level. The economic costs estimation framework could be valuable in estimating farmer compensations and loss and damage of droughts. The results of the study can provide reliable estimates of the costs of climate-change-related extreme weather events, which may help inform macroeconomic and integrated impact assessment models of economic losses (and gains).
Assessing Coastal Resilience from Space
Authors: Luijendijk Arjen , Kras Etienne , Calkoen Floris
Published: March 2024

Satellite imagery proves to be a promising data source to gain insights in historic shoreline behavior over the last 4 decades on a global scale. To enable the use of such a large amount of satellite data, image processing techniques are introduced to interpret such large datasets. Furthermore, Machine Learning (ML) algorithms allow for an extra in-depth understanding of the shoreline dynamics, while growing computational power and standardization of ML packages, opens possibilities for studying shoreline dynamics and their drivers on a global scale.In this way, human drivers, such as nourishments, ports, coastal structures, and natural drivers, such as relative sea level rise, inlet systems, and storms, can be identified across the globe. The high spatial and temporal resolution of this information yields more comprehensive understanding of our coasts and their resilience to cope with a changing climate. This is not only of great added value in data-poor environments, but it will also allow for more cost-effective coastal monitoring in data rich environments as the necessity of in-situ measurements will reduce in future. Furthermore, information on the governing drivers for local coastal change is one of the key elements required for shoreline predictions.Providing such a prediction for future shoreline positions is just one example of a climate service. To prepare coastal zones for a changing climate in the future, coastal managers are demanding various other climate services to efficiently access and use state-of-the-art data on projections related to flooding, erosion, subsidence, vulnerability of assets and adaptation measures. The CoCliCo platform will be presented that fulfils the stakeholder needs by providing climate services at a pan-European scale.
Using Solar-Induced Chlorophyll Fluorescence in a Combined Index to Estimate Tree Productivity and Physiology in the 2018 and 2022 European Droughts
Authors: Brown Ross , Rammig Anja , Paulo Darela-Filho João , Buras Allan
Published: March 2024

The European heatwaves of 2018 and 2022 led many parts of the continent into record high temperatures and extremely dry conditions compared to mean temperature and precipitation. This resulted in a decrease in forest productivity and an increase in forest fires and tree death in affected areas. As droughts increase in severity and frequency with global climate change, it is important to investigate how tree species respond to water stress, and how these responses affect ecosystem productivity.Solar-induced chlorophyll fluorescence (SIF) has been useful for estimating gross primary productivity (GPP) and assessing terrestrial carbon fluxes. Even though SIF provides a direct link for energy available for carbon fixation, SIF is heavily affected by canopy structure and sun-sensor geometry. Near-infrared radiance of vegetation (NIRvR) is a recently studied index that provides accurate information about plant canopy structure, solar irradiance, and has a positive, linear relationship with GPP. Mathematically combining SIF, NIRvR, and the enhanced vegetation index (EVI) into one index may consequently better estimate GPP since this method integrates a direct link to photosynthesis, structure, and greenness, respectively. Especially under conditions where water is limited, the combined SIF-NIRvR-EVI index could provide more instantaneous and accurate estimates of productivity and plant health when compared to the individual indices. Indeed, a recent study (Zeng et al. 2021) normalized SIF with NIRvR to calculate fluorescence yield (ΦF), incorporating photosynthesis and plant structure information into one index.  They found that ΦF accurately detects stress-induced limitations in photosynthesis in field-level data, but little is known about how this approach scales up to satellite-level data.To overcome this research gap, we isolate areas in Germany affected by drought with known dominant tree species and analyze how individual and combined measurements of florescence, canopy structure, and greenness respond to the 2018 and 2022 European droughts, as well as normal precipitation conditions (2019 – 2021). This will provide insights into how water stress affects the physiology of tree species and investigate a new combined SIF-NIRvR-EVI index. A random forest model is applied to examine how well the combined index predicts GPP during drought and non-drought conditions. The resulting model outputs are compared to satellite derived GPP products and separately with FLUXNET sites to ground truth the accuracy of the modeled GPP estimates during the study period.
Structures and kinematics of the Cephalonia-Lefkada Transform Fault zone
Authors: Crosetto Silvia , Faccenna Claudio , Ballato Paolo , Mouslopoulou Vasiliki , Begg John , Metzger Sabrina
Published: March 2024

The Cephalonia-Lekfada Transform Fault Zone (CLTFZ) lies at the transition between the continental subduction of Adria microplate underneath Eurasia to the north, and the oceanic subduction of the Nubian plate along the Hellenic Arc to the south.Since its onset, estimated around the Late Miocene-early Pliocene, the CLTFZ is considered to have accumulated between 40 and ~80 km right-lateral displacement, with most of the offset occurring in the last ~5 Ma. Currently, the intense crustal deformation characterising the area results in high seismicity affecting the western part of Lefkada and Cephalonia islands, as demonstrated by several Mw>6 earthquakes that struck this narrow region in the last two decades (Mw 6.2 Lefkada, 2003; Mw 6.1 and Mw 5.9 Cephalonia, 2014; Mw 6.4 Lefkada, 2015).We use conventional structural mapping and geomorphic analysis to identify and measure the main onshore faults and their kinematic evolution along the western coast of Cephalonia and Lefkada islands.We observe that the topography of the islands is mostly tectonically controlled: right-lateral transpression is expressed as elongated ridges, trending ~N15 and delimited by thrusts. Transtensive, NE-SW-trending en échelon faults develop on the ridge crests, where measured fault throw indicates up to 1.5 km NW-SE-directed extension on a single ridge. Similar structures have been recently observed in analogue modelling experiments, forming as Riedel-shears in the later stages of the transpressional system evolution.Inactive, and hence older faults seem to have formed as opening fractures in an extensional regime (~W-E). The fractures are filled with calcite veins displaying multiple growth generations, which suggests formation in a fluid-rich environment. Active faults display at least two generations of striae indicating pure dip-slip and strike-slip/oblique movement. Where visible, the cross-cutting relationship between the two generations indicates a first phase of normal faulting followed by more recent transcurrent faulting. This is in agreement with geodetic data, reporting a dominant horizontal component of movement during the recent earthquakes.The overall fault analysis indicates an important strain partitioning along the CLTFZ, providing relevant constraints to the seismotectonic pattern of the region.
Understanding the variability in the titanium contents of lunar basalts using high-pressure, high-temperature layered partial melting experiments and thermodynamic modelling
Authors: Moitra Himela , Ghosh Sujoy , Mukherjee Tamalkanti , Gupta Saibal
Published: March 2024

 New high-pressure, high-temperature layered partial melting experiments have been performed to simulate the interaction between the last stage, dense Fe-Ti-rich cumulate layer that remained after about 99.5% crystallization of the lunar magma ocean (LMO), and the underlying solidified Mg-rich mantle. For this, a synthetic assemblage of an upper Fe-Ti-rich cumulate layer (6.1 wt.% TiO2 and 39.7 wt.% FeO) and a lower forsteritic olivine layer (Mg# = 92.8) has been taken in 1:4 weight ratio, respectively, and subjected to experiments at pressures ranging between 1 to 3 GPa and temperatures varying between 1100 ⁰C and 1525 ⁰C using a piston cylinder apparatus. In the top cumulate layer, phases such as Fe-rich clinopyroxene, Fe-poor clinopyroxene, pigeonite, orthopyroxene, rutile, ilmenite, quartz and melt were formed, depending upon different P-T conditions. The Fe-Ti-rich basaltic melts (5-18.5 wt.% TiO2, 13-28.6 wt.% FeO, and 35-59 wt.% SiO2) produced in this cumulate layer at different degrees of partial melting approach the lunar mare basalts in their compositions and can be used to explain the huge variation in TiO2 enrichment that is observed in lunar basalts (between 0 to ~17 wt. %). Following LMO crystallization, the last stage dense mineral-melt cumulate layer is expected to undergo a gravitational overturn due to density instability. This work aims to simulate the partial melting of the cumulates in this layer as a result of the overturn. The consequent compositional heterogeneity of the lunar mantle is used to justify the observed variation in Ti-rich basalt compositions on the lunar surface. The basaltic melts produced in these experiments are mostly Al, Mg-poor compared to available lunar basalt samples. However, this deficiency may be addressed by assimilation of these melts into low-Ti, Mg-rich basalt magmas that would have subsequently erupted from the underlying mantle. Simulations of such assimilation using thermodynamic modelling have also been done and the results support this theory. The possible fate of the last stage melt of the LMO has thus been studied and used to understand the variable compositions of lunar basalts.
Spatial and temporal variability of soil GHG fluxes of urban greens
Authors: Bai Xiao , Andreassen Lars , Dogan Gülnur , Butterbach-Bahl Klaus
Published: March 2024

Global urbanization has significantly affected land use, with former agricultural or forested land being used for human settlements and urban green spaces. How this urbanization may have affected the spatial and temporal patterns of soil greenhouse gas (GHG) fluxes, especially those of nitrous oxide (N2O), remains largely unexplored, although a recent study indicated that urbanization accelerates GHG fluxes from soils.In this study, we investigated soil GHG fluxes at Aarhus University Park (AU Park), a public park located in a hilly landscape with different use intensities. Soil GHG fluxes were measured 2-3 times per week over a period of 7 months using a fast chamber approach at about 55 sampling points with different management, vegetation, and landscape position (uphill, slope, foothill, ponds). Specifically, we focused on the identification of GHG flux hot and cold spots, and thereby investigated the temporal persistence of such spatial emission patterns.Our results show that GHG fluxes were highly variable over the observation period, but that major GHG flux hotspots, such as those near a pond, were hotspots at all observation times. In addition, we were able to relate the spatio-temporal variations in soil GHG fluxes to landscape parameters such as slope and exposition, and to soil parameters such as soil organic carbon concentration, pH, and texture.Our measurements show that there are significant spatio-temporal variations in GHG fluxes in urban parks and that these variations are strongly influenced by environmental and landscape parameters. This observation may allow a better scaling of GHG fluxes of urban green spaces and thus a better assessment of how urbanization changes landscape fluxes.
Interactions between tropical low marine clouds and wind profiles using ALADIN/Aeolus
Authors: Titus Zacharie , Chepfer Hélène , Bonazzola Marine
Published: March 2024

Low clouds such as cumulus and stratocumulus cover a great part of the tropical belt all year long. Variables affecting the formation and dissipation of these clouds like the Sea Surface Temperature or humidity have been studied for a long time now. However, wind profiles could previously only be obtained by radiosondes (localized) or airborne measurements (regional). From 2018 to 2023, ESA ALADIN/Aeolus Doppler Wind LIDAR has orbited the Earth, collecting wind profiles at a global scale, between the surface and 20 km of altitude. This instrument has opened new perspectives regarding wind-cloud interactions with co-located low cloud profiles and wind profiles.  In a Large Eddy Simulation Helfer et al.[1]  have shown that wind shear can have an impact on the development of trade wind cumulus clouds in the first kilometers of the atmosphere. Mieslinger et al.[2]  have shown combining ERA5 wind and ASTER imagery, that stronger surface wind are correlated with a more important cloud cover. In our study, we will see how ALADIN/Aeolus can help us to better understand interactions between low clouds and wind with co-located observed wind profiles and cloud profiles. We will focus on the subtropical marine boundary layer, around strong subsidence regions, like the descending branch of the Hadley cell. In these regions, low clouds are present in number and ALADIN is rarely attenuated due to the rare occurrence of mid and high altitude clouds[3].  [1] Helfer et al. - How Wind Shear Affects Trade-wind Cumulus Convection (2020) [2] Mielsinger et al. - How Wind Shear Affects Trade-wind Cumulus Convection (2020)[3] Chepfer et al. - The GCM oriented CALIPSO Cloud Product (CALIPSO-GOCCP) (2010) 
Remote sensing for mapping Natura 2000 habitats in the Brière marshes (France, Loire-Atlantique, 44) : setting up a long-term monitoring strategy to understand changes 
Authors: Lafitte Thomas
Published: March 2024

Context :This work is part of a doctoral thesis being carried out on the marshes of the regional natural park of Brière (France, Loire-Atlantique, 44). The main purpose is to study the changing dynamics of plant formations on the entire sector, in order to enable prospective modeling. The natural park of Brière would like to set up a Biodiversity Observatory in order to gain a better understanding of the functioning of this fast-changing ecosystem. Field sampling efforts are very costly and time-consuming, and are hampered by difficult access to the extensive marsh areas (over 18 000 hectares). As a result, existing maps are not spatially exhaustive and present a simplification of habitats mosaics, making modeling impossible.Purposes :The aim of the study is to (1) map the current distribution of plant communities (2) identify their dynamics through the historical evolution of potential habitats in relation with hydrology, invasion by trees, agro-pastoral and traditional practices and (3) deduce the marsh's capacity for stability and resilience in the coming decades.Materiel and methods :The first step involves an hyperspectral and LiDAR aerial survey across the whole sector. It is completed by the acquisition of a World View 3 scene, in order to assess the contributions of the different types of data. At the same time, floristic field surveys were carried out to characterize the textural and spectral variability of the images. Automatic classification methods were then applied.Based on the results obtained from the mapping, we can assess the quality of the biodiversity in the Brière region. Indeed, plant formations differ in terms of stability and resilience.Main results :A complete mapping of the 18 000 hectare of marshland has been carried out, according to the Eunis declination, at a spatial resolution of 1.30 meter. This includes areas not yet mapped. In addition to these classic communities, we have also added invasive exotic species, which are very present in the Brière region. The relation between biodiversity loss and human factors from the Middle Ages to the present day has been established.Standardized data acquisition and processing methods have been set up to enable long-term changes monitoring.
BBCMap 2023: Assessing methane emissions from open-cut and underground coal mining in eastern Australia
Authors: Borchardt Jakob , Krautwurst Sven , Gerilowski Konstantin , Huhs Oke , Schindewolf Josua , Bovensmann Heinrich , Kumm Martin , McGrath Andrew , Chakravarty Shakti , Junkermann Wolfgang , Hacker Jorg , Bai Mei , Kelly Bryce F. J. , Harris Stephen , Field Robert , Bösch Hartmut , Burrows John P.
Published: March 2024

Methane (CH4) is the second most important anthropogenic greenhouse gas (GHG), and its emissions reduction has been identified as an essential mitigation target to slow down climate change. According to inventories, fossil fuel production and usage account for roughly 17% of the global CH4  emissions, of which approximately 33% originate from coal mining. Accurate identification of coal mining-related CH4 sources and quantification of their annual emission rate is needed for corporate reporting requirements, national inventory verification, and the development of CH4 mitigation strategies.A previous study estimated CH4 emissions for six coal mines in the Bowen Basin in Queensland, Australia, using TROPOMI satellite measurements. It covered a sub-area of the Bowen Basin, where coal is mined at over 40 active mining locations distributed over 60,000 km2. The study showed a significant discrepancy compared to inventory estimates by a factor of 7 during 2018 and 2019.To further verify satellite estimates and improve knowledge of the distribution, persistence, and strength of emissions of this mining region, the Bowen Basin CH4 Mapping (BBCMap) Campaign was conducted in September-October 2023, funded by and performed in collaboration with UNEP's International Methane Emissions Observatory. During this campaign, two HK36 Eco-Dimona research aircraft carrying complementary sensing instrumentation were deployed. The MAMAP2D-Light (Methane Airborne MAPper 2D – Light) imaging spectrometer for estimating atmospheric CH4 and CO2 column anomalies and a lidar for topography scans were deployed on one DIMONA HK36 research aircraft, while the second identical aircraft was equipped with an in-situ payload consisting of an LGR OA-ICOS gas analyser for simultaneous measurements of atmospheric CH4, CO2, and water vapor concentrations, a turbulence probe for wind statistics, and a bag sampler for collecting multiple gas samples during each flight for later 13C isotope analyses in the laboratory. This two-aircraft strategy allowed coordinated measurements of CH4 emissions from different coal mines with both remote sensing and in-situ instruments and simultaneous wind measurements, which is essential for deriving a robust flux estimate.During the campaign, 39 flights were conducted, covering approximately 33 mines across roughly 20,000 km2, focussing on the northern part of the Bowen Basin. Preliminary MAMAP2D-Light measurements of atmospheric CH4 column anomalies and emission estimates for both open-cut and underground coal mines will be presented and discussed.
A complete energy analysis of ocean background and available potential energy over the past century
Authors: Tailleux Remi
Published: March 2024

After many decades of relative stability, the heat content of the oceans has been increasing at a relatively steady rate since the late 70’s, as confirmed by the analysis of many observational datasets. How global warming affects the available potential energy (APE) of the oceans, however, has received comparatively much less attention, yet is important to assess changes in the strength of wind-driven gyres and Antarctic Circumpolar Current for instance. In this talk, I will contrast the temporal changes over the past century of the APE and background potential energy (BPE) of the oceans based on the analysis of the EN4 dataset, by making use of the most recent local theory of APE. Results show that temporal changes in the BPE mimic that of the ocean heat content estimated in terms of potential temperature or Conservative Temperature. In contrast to the ocean heat content, the total APE of the oceans does not exhibit any marked trend. In this talk, I will discuss how regional APE estimates differ from the global APE estimate, to identify whether global warming has any detectable impact the large-scale ocean circulation features.
Skill of wind resource forecasts on the decadal time scale
Authors: Lochbihler Kai , Lopez Ana , Lizcano Gil
Published: March 2024

Accurate forecasts of the natural resources of renewable energy production have become not only a valuable but a crucial tool for managing the associated risks of specific events, such as wind droughts. Wind energy, alongside with solar power, now provide a substantial part to the renewable energy share of the global energy production and growth in this sector will most likely further increase. The naturally given fluctuations of wind resources, however, pose a challenge for maintaining a stable energy supply, which, at the end of the chain, can have an impact on the energy market prices.Operational short-term forecasting products for the wind energy sector (multiple days) are already commonly available and seasonal to sub seasonal forecasting solutions (multiple months) can provide valuable skill and are gaining in popularity. On the other side of the spectrum, typically on a time scale of multiple decades, we find risk assessment based on climate change projections. In between the long and short term time scales, however, there is a gap that still needs to be filled to achieve seamless prediction of risks that are relevant for the energy sector: decadal predictions.Here, we present the results of an evaluation study of a multi-model decadal prediction ensemble (DCPP) for a selection of wind development regions in Europe. The evaluation is based on multiple decades long hindcasts and carried out with a focus on the skill of predicting specific event types of wind resource availability in a probabilistic context, alongside with basic deterministic skill measures. We further investigate specific event constellations and their large-scale drivers that, in combination, can provide windows of opportunity with enhanced predictive skill. We conclude with a discussion on how this hybrid approach can be used to potentially increase not only forecast skill but also the trust of the end user.
Root functional strategies as drivers of the functional composition of soil fungal communities
Authors: Hennecke Justus , Weigelt Alexandra
Published: March 2024

Plant functioning heavily relies on roots. Many root functions, however, are intrinsically linked with fungal mutualists or can be reduced by fungal antagonists. Consequently, a better knowledge of the factors shaping fungal community composition is essential for our understanding of plant and ecosystem functioning. Beyond abiotic factors, the identity and composition of the plant community itself influence the soil fungal community. Depending on their root functional strategies, plants may engage differently with the soil microbial community. The root economics space (RES) has advanced our understanding of these root functional strategies, and the close association of the collaboration gradient with mycorrhizal fungi indicates that root traits could provide insights into soil fungal communities.We hypothesize that root trait strategies along the collaboration and conservation axes of the RES, as well as plant diversity, influence the composition of soil fungal communities. For instance, we hypothesize a decrease in the abundance of plant pathogenic fungi in more diverse plant communities and those with well-defended species. Higher plant defense is typically associated with root traits related to high mycorrhization ("outsourcing") and high tissue density ("slow"). Arbuscular mycorrhizal fungi are expected to exhibit contrasting trends compared to pathogens. We expect saprotrophic fungi to be mostly affected by the change in litter quality along the conservation gradient (‘fast-slow’). Hence, our study explores the interactive and additive effects of root trait gradients and plant species richness on soil fungal communities.In plots of the Jena Experiment with varying levels of plant species richness, we measured root traits at the community level and sampled soil microbial communities. Using amplicon sequencing, PLFA analyses, and microbial respiration measurements, we determined relative abundances of mycorrhizal mutualists, plant pathogens, and saprotrophs as well as absolute fungal and microbial biomass. Our findings indicate a significant decrease in the diversity and relative abundance of plant pathogenic fungi in plant communities with outsourcing root strategies. This highlights the central role of the root collaboration axis in shaping soil fungal communities beyond the direct link with arbuscular mycorrhiza. Changes in fungal and microbial biomass, however, are strongly determined by plant diversity and not driven by root traits.
Improving global evaporation estimation using GRACE and GRACE-FO satellite data assimilation
Authors: Haghdoost Shekoofeh , Koppa Akash , Lievens Hans , Miralles Diego G.
Published: March 2024

The accurate monitoring and prediction of land water cycle components are crucial for applications in climate, hydrology, and agriculture. However, the remote sensing of ecohydrological variables, though essential, still faces challenges, especially in estimating non-directly observable factors like evaporation. Utilizing GRACE and GRACE-FO satellite data has the potential to improve global evaporation estimates and therefore to enhance our ability to understand and manage these components. Such advancements in global evaporation estimation can furthermore contribute to addressing future water management challenges, including mitigating the impacts of drought and potential groundwater reductions. To date, several remote sensing assets have been underused within the context of global evaporation estimation, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions, active since 2002 and 2018, respectively. These missions can play a key role in representing surface and subsurface processes related to water redistribution, providing estimates of Terrestrial Water Storage (TWS), and enriching our ability to navigate global water resource complexities.The goal of this research is to improve the estimates of evaporation from the Global Land Evaporation Amsterdam Model (GLEAM) by using GRACE and GRACE-FO observations. GLEAM is a set of algorithms dedicated to estimating terrestrial evaporation based on satellite observations of meteorological drivers of terrestrial evaporation, vegetation characteristics, and soil moisture (Miralles et al. 2011). In this regard, we use GRACE observations in a data assimilation approach, based on Newtonian Nudging with model and observation errors defined by triple collocation, to improve the evaporation estimates of GLEAM. The study period comprises 20 years, between January 2003 and December 2022. Preliminary results indicate that the data assimilation output is closer to reality, for instance for estimating evaporation changes in Brazil and South America.  