bluuebunny/crossref_metadata_2025 huggingface dataset:
Advancing compound modelling of tropical cyclone wind, surge and rain impacts – now and in a changing climate
Authors: Meiler Simona , Sarhadi Ali , Emanuel Kerry , Bresch David N.
Published: March 2022

Intense precipitation from tropical cyclones (TCs), typically accompanied by wind-driven storm surges and highly destructive winds, constitutes a significant threat for compound flooding and wind-driven impacts in many coastal regions worldwide. However, most present TC risk assessment methods only consider wind as the driving hazard and thus underestimate impacts emerging from compounding TC sub-hazards. Further, it is crucial to understand how this risk will shift and intensify in a warming climate. We thus present a coupled, physics-based modeling approach for the coastal area of Metropolitan Manila (PHL) to explicitly represent TC rainfall-induced freshwater flood, TC wind-driven storm surges, and direct impacts from TC wind for present and future climate. We use a large set of synthetic TCs generated from historical climate data (1985-2014) and from the late 21st century (2071-2100) SSP585 warming scenario to simulate TC wind fields and rainfall intensity data. Our modelling chain includes a hydrodynamical component to convert TC precipitation to freshwater flood and model wind-driven storm surges. We evaluate the compound socio-economic impacts from the TC sub-hazards using a state-of-the-art, open-source probabilistic damage model (CLIMADA). Ultimately, our advances in TC impact modelling can be applied in vulnerable coastal regions worldwide, enabling better-informed adaptation decisions and mitigation strategies.
Airborne flux measurements of ammonia over the Southern Great Plains using chemical ionization mass spectrometry
Authors: Schobesberger Siegfried , D'Ambro Emma L. , Lee Ben H. , Peng Qiaoyun , Pekour Mikhail S. , Fast Jerome D. , Thornton Joel A.
Published: March 2022

Ammonia (NH3) plays an important role in atmospheric and environmental chemistry, from the formation of inorganic and organic aerosol, to soil acidification and nutrient cycles. Its dominant source are anthropogenic emissions, primarily from agricultural activities, and it thereby contributes substantially to fine-particle pollution in many regions. However, there are high uncertainties in attributing atmospheric NH3 to specific sources, and current emission inventories substantially underestimate many major point sources. The quantification of NH3 is challenging, due to the wide range of ambient mixing ratios and its infamous propensity to interact with surfaces, causing losses and slow response times.

In this study, we present a new technique for detecting NH3 using a chemical ionization mass spectrometer (CIMS). The CIMS was deployed on a G-1 aircraft during the Holistic Interactions of Shallow Clouds, Aerosols, and Land Ecosystems (HI-SCALE) campaign over Oklahoma, specifically around the ARM Southern Great Plains field site, in 2016. The instrument was modified to enable quantifiable airborne measurements throughout tropospheric pressures, and to alternatingly use iodide anion and deuterated benzene cation ionization. In this mode, and aided by a high-flow core-sampling setup, we obtained a formidable device for measuring in-situ mixing ratios of NH3. Measured NH3 mixing ratios spanned from <10 to 100s of parts per trillion in the free troposphere, to sharp plumes of highly elevated mixing ratios (10’s of parts per billion) downwind from a fertilizer plant. These plumes are of the order expected based on the U.S. Environmental Protection Agency’s National Emissions Inventory (NEI). The high sensitivity and response time of ~1 s allowed us to also calculate vertical NH3 fluxes via eddy covariance. We used the continuous wavelet transform method to maximize the spatial resolution of the derived fluxes, remaining limited to ~1-2 km by the flight altitudes and related turbulence scales. Together with flux footprint considerations, the measurements let us constrain the NH3 emission rates for ubiquitous agricultural area sources in rural Oklahoma. Typically, the derived area emission rates clearly exceeded the values provided by the NEI. In addition, our measurements captured large point sources that appeared to be missing in the NEI, at least one identified as a large cattle farm.
The Zakharov Equation as a Model for Wind Waves: Nearby Integrability, Hamiltonian Perturbations and Multiply Periodic Fourier Series
Authors: Osborne Alfred
Published: March 2022

The Zakharov equation is a fundamental equation of water waves that is used as a dynamical model for wind wave growth/decay. A nearby Lax integrable version of the Zakharov equation is studied and subsequently a Hamiltonian perturbation provides a close approximation of the Zakharov equation itself.  Theorems of Kuksin, and Baker and Mumford are used to develop the algebraic-geometric solutions of the Zakharov equation in terms of the associated Its-Matveev formula. A subsequent derivation of a multiply periodic Fourier series solution is found which includes the coherent structure solutions (breathers) and cascading. The correlation function is computed and the space/time evolution of the Power spectrum is given analytic form, including a wind-wave transfer function appropriate for multiply periodic Fourier series. Some advantages of this method over classical kinetic equations are that the modulational instability is included together with coherent structure breather solutions. Furthermore, the weak interaction assumptions are no longer necessary in this new formulation, which retains the full nonlinear interactions of the Zakharov equation. 
A hybrid statistical-dynamical method to downscale global climate models over Europe
Authors: Boé Julien , Mass Alexandre
Published: March 2022

To characterize the impacts of climate change, robust high-resolution climate change information is generally needed. The resolution of global climate models is currently too coarse to provide directly such information. A specific spatial downscaling step is therefore generally needed, either (1) dynamical downscaling with regional climate models or (2) statistical downscaling.

In this study, we present a new hybrid statistical-dynamical downscaling approach, intended to combine the respective strengths of statistical and dynamical downscaling, while overcoming their respective limitations. This hybrid method aims to emulate regional climate models and is based on a constructed analogues method.

Contrary to dynamical downscaling, the computational cost of the method is low, allowing to downscale a large number of global climate projections and therefore to correctly assess the climate uncertainties in impact studies. Contrary to statistical downscaling, the method does not rely on the assumption that the downscaling relationship established in the present climate with observations remains valid in the future climate perturbed by anthropogenic forcings. Therefore, the hybrid approach should be as robust as regional climate models in projecting future climate change.

In this presentation, the hybrid statistical-dynamical downscaling method is first presented. Elements of evaluation, in a perfect model framework based on an ensemble of regional climate models over Europe, are then shown and discussed to demonstrate the interest of the method and its applicability to study future climate changes over western Europe. Finally, results of the application of the method to downscale global climate projections over western Europe are shown, and important implications of the results are discussed.   
Building an InSAR-based database to support geohazard risk management by exploiting large ground deformation datasets 
Authors: Béjar-Pizarro Marta , Ezquerro Pablo , Guardiola-Albert Carolina , Aguilera Alonso Héctor , Sanabria Pabón Margarita Patricia , Monserrat Oriol , Barra Anna , Reyes-Carmona Cristina , Mateos Rosa Maria , García López Davalillo Juan Carlos , López Vinielles Juan , Bru Guadalupe , Sarro Roberto , Galve Jorge Pedro , Tomás Roberto , Rodríguez Gómez Virginia , Mulas de la Peña Joaquín , Herrera Gerardo
Published: March 2022

The detection of areas of the Earth’s surface experiencing active deformation processes and the identification of the responsible phenomena (e.g. landslides activated after rainy events, subsidence due to groundwater extraction in agricultural areas, consolidation settlements, instabilities in active or abandoned mines) is critical for geohazard risk management and ultimately to mitigate the unwanted effects on the affected populations and the environment.

This will now be possible at European level thanks to the Copernicus European Ground Motion Service (EGMS), which will provide ground displacement measurements derived from time series analyses of Sentinel-1 data, using Interferometric Synthetic Aperture Radar (InSAR). The EGMS, which will be available to users in the first quarter of 2022 and will be updated annually, will be especially useful to identify displacements associated to landslides, subsidence and deformation of infrastructure.  To fully exploit the capabilities of this large InSAR datasets, it is fundamental to develop automatic analysis tools, such as machine learning algorithms, which require an InSAR-derived deformation database to train and improve them.  

Here we present the preliminary InSAR-derived deformation database developed in the framework of the SARAI project, which incorporates the previous InSAR results of the IGME-InSARlab and CTTC teams in Spain. The database contains classified points of measurement with the associated InSAR deformation and a set of environmental variables potentially correlated with the deformation phenomena, such as geology/lithology, land-surface slope, land cover, meteorological data, population density, and inventories such as the mining registry, the groundwater database, and the IGME’s land movements database (MOVES). We discuss the main strategies used to identify and classify pixels and areas that are moving, the covariables used and some ideas to improve the database in the future. This work has been developed in the framework of project PID2020-116540RB-C22 funded by MCIN/ AEI /10.13039/501100011033 and e-Shape project, with funding from the European Union’s Horizon 2020 research and innovation program under grant agreement 820852.
Lithospheric domains of the West and Central African rift system based on Terracing and Cluster analysis
Authors: Fosso Teguia M Estelle Eric , Ebbing Jörg , Haas Peter
Published: March 2022

We present results of cluster analysis and geophysical modelling of the West and Central African rift system, where we integrate seismological and satellite data. For a description of lithospheric domains, two different methods based on seismic tomography and satellite gravity data have been used. First, the terracing method using the shape index, has been applied to the gravity field in order to enhance the signal of the large-scale tectonic units. In addition, the K-means cluster method (which is an unsupervised machine learning algorithm) has been applied to a seismic tomography model over the area.

Both models are compared and interpreted towards similarities and differences. The preliminary analysis based on K-means clustering of seismic tomography shows that the West and Central African rift system and its surroundings can be divided into at least three clearly distinct tectonic domains: The Northern part of the Congo craton, the Eastern part of the West African craton and an area in between. In addition, the preliminary analysis of the terracing of satellite gravity data, confirms the location of both the Congo and the West African craton, but also splits the area in between into two known tectonic units, the Southern part of the Saharan meta-craton and the West and Central African rift system in the center.

The cluster analysis is also pointing to differences at crustal and upper mantle level and is the first step towards the evolution of a lithospheric scale model. In the model, we integrate our tectonic domain analysis with the existing seismic Moho depths estimate and other information.
Urban water neutrality at different scales: CityPlan design and evaluation framework
Authors: Puchol-Salort Pepe , Lu Jiaying , Boskovic Stanislava , Mijic Ana , Dobson Barnaby , van Reeuwijk Maarten
Published: March 2022

London aims to build more than half a million households over the next 10 years to cope with the growing demand for housing in the UK. In this future scenario, urban water security levels will be threatened due to new development pressures combined with the climate emergency and exponential population growth in the city. In addition to this, there is a lack of agreement between the policy and decision-making sectors to decide what can be accepted as a sustainable urban development project and which are the physical and decision boundaries inside the city (i.e., while boroughs and wastewater zones present decision boundaries, new urban developments have physical boundaries only). In our previous work, we developed a new concept for urban Water Neutrality (WN) inside an operational framework called CityPlan to frame the concerns about rising water stresses in cities. This framework integrates spatial data with an integrated urban water management model, enabling urban design at systems level and delivering a new index that assesses possible future scenarios. Despite several studies related to WN, little evidence is yet available in the literature of how urban water neutrality can be achieved at different urban scales and if results might vary depending on the scale studied.

In this work, we expand the CityPlan framework and present an innovative evaluation approach that sets several urban indicators to be tested at different urban scales. As part of the evaluation toolkit of CityPlan, we also develop the Water Efficiency Certificate (WEC) by boroughs using two novel criteria: the Housing Age Indicator (HAI) and the Device Efficiency Score (DES). The WEC evaluates the current situation of household water consumption and can be used to support predictions of water consumption under different scenarios, to study the potential for retrofitting existing residential buildings, and to develop water-efficient households. In the end, the full development of the CityPlan framework will provide a clear vision to contextualise water neutrality in urban water systems and its key role in urban water security at different urban scales.
On Repeating Earthquakes in the Northern Chilean Subduction Zone
Authors: Folesky Jonas , Hofman Rens , Kummerow Jörn
Published: March 2022

At the northern Chilean subduction zone, where the IPOC network has been monitoring seismicity since 2007, we have identified multiple families of repeating earthquakes. High data quality and long observation time allow analyzing these sequences in detail.
Often repeaters are searched to be used as creep proxies and their spatio-temporal cumulative displacement is compared with the tectonic plate convergence rate or GPS based slip rate estimates for smaller fault patches. Repeaters can be classified into periodic, pseudo-periodic or aperiodic types. Put into relation with large earthquakes such as the 2014 MW8.1 Iquique earthquake, repeaters may be described as continuous or burst type families. A precondition for such an analysis is that events are collocated and show highly similar mechanism. This is usually ensured via high cross correlation values between waveforms or by catalog location, or both. Errors in grouping would heavily bias the analysis for individual groups.
Therefore, we not only use cross correlation values, but we analyze the intra-family relations in detail. Events are relocated relative to each other based on phase based cross correlation refined s-p travel time differences. Rupture sizes are estimated and intra-family rupture histories are resolved. Having confirmed the characteristics of true repeating earthquake families in this way, we make the classifications and compute the slip rates mentioned above.
This study shows that the concept of repeating earthquakes holds beautifully in the case of the northern Chilean subduction zone. Repeater families repeatedly rupture the same patches, and they are observed to respond different to the 2014 Iquique with a strong dependence on their location. Particularly, the time around the Iquique megathrust event shows very interesting patterns in several families. We observe clear precursor patterns, burst reactions and unresponsive families simultaneously.
SnowPEx+: The International Snow Products Intercomparison and Evaluation Excercise 2015-2020
Authors: Nagler Thomas ,   SnowPEx Team
Published: March 2022

Satellite observations are the only means for timely and complete observations of the global snow cover. A range of different satellite snow products is available, the performance of which is of vital interest for the global user community. We provide an overview on goals and activities of the SnowPEx+ initiative, dedicated to the intercomparison of northern hemispheric and global satellite snow products, derived from data of long-term operational as well as recently launched satellites. SnowPEx+ is the continuation of SnowPEx (2014-2017), carried out as an international collaborative effort under the umbrella of Global Cryosphere Watch / WMO and funded by ESA.

SnowPEx+ focuses on two parameters of the seasonal snowpack, the snow extent (SE) from medium resolution optical satellite data (Sentinel-3, VIIRS, MODIS, AVHRR, etc.) and the snow water equivalent (SWE) from passive microwave satellite data. Overall, 15 hemispheric and global SE products (binary and fractional SE) and two SWE products are participating in the experiment. For intercomparison, daily SE products are transformed to a common map projection and standardized SnowPEx protocols are applied, elaborated by the international snow product community. The SE product evaluation applies statistical measures for quantifying the agreement between the various products, including the analysis of spatial patterns. Validation of SE products uses as benchmark high resolution snow maps from about 150 globally distributed Landsat scenes acquired in different climate zones, under different solar illumination conditions and over various land cover types. This snow reference data set, based on various retrieval algorithms, is generated and evaluated by the SnowPEx+ High Resolution Snow Products Focus Group. In-situ snow data from several organisations in Europe, North America and Asia are also used for validating the satellite SE and SWE products. SWE products are also inter-compared with gridded snow products from land surface models driven by atmospheric reanalysis data. In addition, the multi-year trends of the various SE and SWE products are evaluated. We provide an overview on the snow products, discuss the validation and intercomparison protocols, and report on preliminary results from the intercomparison and validation of various snow products.
Boundary layer height variability in winter in southeastern France for typical meteorological situations
Authors: Riandet Aurélie , Xueref-Remy Irène , Blanc Pierre-Eric , Popovici Ioana , Goloub Philippe , Lelandais Ludovic , Armengaud Alexandre
Published: March 2022

The boundary layer is the main dilution factor of gas and aerosol sources but high uncertainties remain on its variability, in particular in southeastern France. Atmospheric dynamics that take place there are complex due to the presence of the sea in the west and the south and by hills in the north and the east. This study is based on a field campaign performed in this area from November 7th, 2019 to January 27th, 2020. Four sites were selected : an urban site equipped with a CIMEL CE376 lidar in the city center of Marseille, two suburban sites (Nîmes and Marignane) equipped with a radiosounding facility and a Vaisala CL31 ceilometer respectively, and a rural site (Observatoire de Haute-Provence – OHP) equipped with radioundings and a CIMEL CE376 lidar, unfortunately encountering many issues during that period. Wind measurements are available for each site. The boundary layer height was retrieved with both the Richardson method and the wavelet transform one. Due to the complexity of aerosol layers encountered above Marseille, the boundary layer height temporal variability is investigated through 2 typical meteorological situations encountered in Marseille, e.g. sea/land breezes and Mistral (regional-northwestward-colder wind blowing over southeastern France from Rhône valley). To better trace the origin of the air masses, the depolarisation ratio from lidar is used. Comparisons between the four sites contribute to describe the spatial variability of the boundary layer height in south-eastern France.
Seismically induced Landslide hazard assessment based on the spatial distribution of the slope strength demand in the Western Himalayas
Authors: Gupta Kunal , Satyam Neelima
Published: March 2022

Earthquake-induced landslides are among the most common seismic hazards in Indian Himalayan high terrains, claiming hundreds of lives and infrastructural losses. Uttarakhand state is in the Western Himalayas and comes under high seismic activity zones as per the seismic code of India. However, a detailed seismically induced landslide hazard assessment is unavailable for the region. Therefore, a parametric time probabilistic approach was used to evaluate the co-seismic landslide hazard in Uttarakhand. Characteristics of the seismicity affecting the area were considered to estimate the critical acceleration (Ac)x(p,t) that slopes should have to limit the probability of exceedance of Newmark displacement value x within time t. Initially, occurrence probabilities for different degrees of seismic shaking for a time frame of 50 years were calculated in terms of Arias intensity. Then, the spatial distribution of the slope strength demand was mapped using the empirical relationship of the Newmark displacement with Arias intensity and critical acceleration. Newmark displacement of 2 and 10 cm were considered critical thresholds with a 10% probability of exceedance. The obtained results suggested that the significant part of the region along the Main Boundary Thrust (MBT) and Main Central thrust (MCT) have Arias Intensity value greater than 2 m/s. Higher Arias intensity values of approximately 4.5 m/s for soil slope conditions and 3 m/s for rock slope conditions were observed throughout the lesser Himalayan zone. In these areas, for the thresholds mentioned above, the exceedance probability in 50 years reaches 50% in the case of 0.32 m/s for soil slope conditions and 70% in the case of 0:11 m/s for rock slope conditions. By comparing the anticipated strength demand with the actual critical acceleration values computed from slope material parameters and slope angle, the resultant slope strength demand maps could offer the basis for determining if particular slopes have a considerable failure probability.
ENES Data Space: an open, cloud-enabled data science environment for climate analysis
Authors: Antonio Fabrizio , Elia Donatello , Giannotta Andrea , Nuzzo Alessandra , Levavasseur Guillaume , Ben Nasser Atef , Nassisi Paola , D'Anca Alessandro , Fiore Sandro , Joussaume Sylvie , Aloisio Giovanni
Published: March 2022

The scientific discovery process has been deeply influenced by the data deluge started at the beginning of this century. This has caused a profound transformation in several scientific domains which are now moving towards much more collaborative processes. 

In the climate sciences domain, the ENES Data Space aims to provide an open, scalable, cloud-enabled data science environment for climate data analysis. It represents a collaborative research environment, deployed on top of the EGI federated cloud infrastructure, specifically designed to address the needs of the ENES community. The service, developed in the context of the EGI-ACE project, provides ready-to-use compute resources and datasets, as well as a rich ecosystem of open source Python modules and community-based tools (e.g., CDO, Ophidia, Xarray, Cartopy, etc.), all made available through the user-friendly Jupyter interface. 

In particular, the ENES Data Space provides access to a multi-terabyte set of specific variable-centric collections from large community experiments to support researchers in climate model data analysis experiments. The data pool of the ENES Data Space consists of a mirrored subset of CMIP datasets from the ESGF federated data archive collected by using the Synda community tool in order to provide the most up to date datasets into a single location. Results and output products as well as experiment definitions (in the form of Jupyter Notebooks) can be easily shared among users through data sharing services, which are also being integrated in the infrastructure, such as EGI DataHub.

The service was opened in the second part of 2021 and is now accessible in the European Open Science Cloud (EOSC) through the EOSC Portal Marketplace (https://marketplace.eosc-portal.eu/services/enes-data-space). This contribution will present an overview of the ENES Data Space service and its main features.
Towards a high-resolution vS crustal velocity model for the Ivrea Geophysical Body: constraints from seismic ambient noise tomography
Authors: Scarponi Matteo , Kvapil Jiri , Vecsey Ludek , Plomerová Jaroslava , Working Group IvreaArray , Working Group AlpArray
Published: March 2022

The arc of the Western Alps is characterized by a complex crustal structure. Lower-to-middle crustal composition outcrops are exposed in the Ivrea-Verbano Zone (IVZ) and a major crustal anomaly, known as Ivrea Geophysical Body (IGB), presents dense and seismically fast rocks right below the surface. Understanding better their relation provides a key to refine our understanding of orogeny formation mechanisms.

We performed seismic ambient noise tomography using data from the IvreaArray and the AlpArray Seismic Network, selected within a radius of ca. 100 km around the study area. Previous seismic investigations provided knowledge on the crustal structure in the Western Alps, by means of active refraction seismics and of more recent local earthquake and ambient noise tomography at regional scales (e.g. Solarino et al. 2018 Lithos, Lu et al. 2018 GJI). Recently, gravity data and receiver function analysis imaged the IGB as a dense and fast seismic anomaly, related to upper mantle material, reaching up to few km depth below sea level (Scarponi et al. 2021 Frontiers). However, local high-resolution constraints on the absolute vS distribution remain unknown.

We used raw summer seismic data (June to September) across 3 years of recording, and computed daily ambient noise cross-correlation traces, for all the available station pairs (61 stations in total) in the 2-20s period range. Daily cross-correlations were stacked and processed to extract Green’s functions. Subsequently, we performed frequency-time analysis to get group velocity dispersions for the fundamental mode of surface Rayleigh waves. We computed 2D surface group velocity maps at each period, which clearly show the slow sediment area of the Po Plain, and the fast IGB structure within the crust.

We are going to use the 2D group velocity maps to derive local dispersions curves and invert for 1D vS-depth profiles with the use of the Neighborhood Algorithm, to produce a 3D vS velocity model for the IVZ at high-resolution. This will also provide new geophysical constraints in the target area of the scientific drilling project DIVE (www.dive2ivrea.org) and reliable information for crustal corrections, which are necessary for upper mantle studies in such a complex area.
Linking peatland redox conditions to pore water composition and greenhouse gas production
Authors: van der Velde Ype , Boonman Jim , Harpenslager Sarah Faye , van Dijk Gijs , Smolders Fons , van Huissteden Ko , Hefting Mariet
Published: March 2022

As a result of limited decomposition under prevalent reducing conditions often over a timespan of thousands of years, organic soils store ±600 Gt of carbon. Currently, almost 14% of the global peat carbon storage is threatened by degradation, which was responsible for 2% of the anthropogenic greenhouse gas emissions in 2019. Decomposition of peat is the result of metabolic processes of the microbial community in the soil (also referred to as microbial respiration or oxidation). This microbial respiration activity strongly depends on biogeochemical conditions and especially to the availability of (alternative) electron acceptors in the soil profile. The redox potential is a reflection of the dominant electron acceptors present and the prevailing biogeochemical processes in the soil. Knowledge on the correlation between electron acceptor availability and redox conditions in peat soils remains however confined to laboratory studies, in which the sample is likely to be disturbed and boundary conditions are artificial. In this study we compared 2 years of continuous field measurements of redox potential with the chemical composition of over 1500 pore water samples, collected at different depths (20, 40 and 70 cm) in five agricultural peat soils throughout the Netherlands. The aim of this research is to identify the important metabolic processes for distinctive ranges in redox and pH under field conditions and compare these with known theoretical thermodynamic equilibria. We show that redox conditions are strongly correlated with products of anaerobic metabolism. Additionally, we present breakpoints for zones with distinct metabolic processes and biogeochemical states, which we use to interpret  time series of redox depth profiles. With these results we demonstrate the value of in-situ redox measurements to understand peat soil respiration rates and associated greenhouse gas emission from organic soils.
Eustatic change modulates exhumation in the Japanese Alps
Authors: King Georgina , Ahadi Floriane , Sueoka Shigeru , Herman Frédéric , Anderson Leif , Gautheron Cécile , Tsukamoto Sumiko , Stalder Nadja , Biswas Rabiul , Fox Matthew , Delpech Guillaume , Scharwtz Stéphane , Tagami Takahiro
Published: March 2022

The exhumation of bedrock is controlled by the interplay between tectonics, surface processes and climate. The highest exhumation rates of cm/yr are recorded in zones of highly active tectonic convergence such as the southern Alps of New Zealand or Himalayan syntaxes, where high rock uplift rates combine with very active surface processes. Here, we use a combination of different thermochronometric systems, and notably trapped-charge thermochronometery, to show that such rates also occur in the Hida Range, Japanese Alps. Our results imply that cm/yr rates of exhumation may be more common than previously thought.

The Hida Range is the most northern and most extensive of the Japanese Alps, and reaches elevations of up to 3000 m a.s.l. The Hida Range is thought to have uplifted in the last 3 Myr in response to E-W compression and magmatism. Our study focuses on samples from the Kurobe gorge, which is one of the steepest gorges in Japan. Previous work has shown that exhumation rates in this region are exceptionally high, as documented by the exposure of the ~0.8 Ma Kurobe granite (Ito et al., 2013) in the gorge. We combined 12 new zircon (U-Th/He) ages and 11 new OSL-thermochronometry ages together with existing thermochronometric data to investigate the late Pleistocene exhumation of this region.

We found that exhumation rates increased to ~10 mm/yr within the past 300 kyr, likely in response to river base-level fall that increased channel steepness due to climatically controlled eustatic changes. Our thermochronometry data allow the development of time-series of exhumation rate changes at the timescale of glacial-interglacial cycles and show a four-fold increase in baseline rates over the past ~65 kyr. This increase in exhumation rate is likely explained by knickpoint propagation due to a combination of very high precipitation rates, climatic change, sea-level fall, range-front faulting and moderate rock uplift. Our data show that in regions with horizontal convergence, coupling between climate, surface processes and tectonics can exert a significant effect on rates of exhumation.

References

Ito, H., Yamada, R., Tamura, A., Arai, S., Horie, K., Hokada T., 2013. Earth’s youngest exposed granite and its tectonic implications: the 10-0.8 Ma Kurobegawa Granite. Scientific Reports 3: 1306.

 
Assessment of wave-current interactions on the Mediterranean Sea dynamics
Authors: Moulin Aimie , Clementi Emanuela
Published: March 2022

The latest NEMO version (v4.2-RC, Release Candidate) has been updated to include new processes related to wave-current interactions. This study is assessing the impact of those new developments, especially the effect of the wave-induced mixing in the Mediterranean sea dynamics. A set of sensitivity experiments are performed using the hydrodynamic model NEMO v4.2-RC coupled with the spectral wave model WaveWatchIII (WW3) v6.07 through the OASIS library. The configuration is based on the operational Copernicus Marine Service Mediterranean forecasting physical system (MedFS). Both models are implemented at 1/24° resolution and are forced by ECMWF 1/10° horizontal resolution atmospheric fields. The models are one-way coupled therefore the wave model is sending fields every hour to the hydrodynamic model. Two-year (2019–2020) numerical experiments are carried out in both uncoupled and coupled mode. In order to validate the system, numerical results are compared with in-situ and satellite data. This study is focused on the impact of the coupling on upper-ocean properties (such as temperature, salinity and surface currents) and mixed layer depth, at mesoscale. The sensitivity of the ocean dynamic to the wave-current interaction is also evaluated during a specific extreme event. Numerical simulations show a global decrease of the wind stress in the Mediterranean Sea due to the interaction with waves. The wave-induced drag coefficient leads only to minor improvements in the circulation fields. The shear of the current in the upper meters is almost due to the Stokes-drift as the mixing by waves is reducing the shear of the mean current. The modifications of the Turbulent Kinetic Energy vertical closure scheme and the inclusion of the Langmuir turbulence lead to an increase in the mixing in specific areas, thus helping to deepen the Mixed Layer Depth.
Geomorphometric constraints on the development of the Wutach capture
Authors: Schwanghart Wolfgang , Tofelde Stefanie , Scherler Dirk , Ott Richard , Ludwig Andreas , Landgraf Angela
Published: March 2022

The Wutach capture is one of the most prominent river captures in central Europe. Subsidence of the Upper Rhine Graben and subsequent incision of tributaries to the Hochrhein led to the piracy of the Danube-Wutach draining the Feldberg region of the Black Forest at ~18 ka. The sudden lowering of the base level led to headward incision of the Wutach and the formation of numerous fluvial knickpoints along the trunk river and its tributaries. These knickpoints represent excellent markers that enable testing several hypotheses including the role of bedrock erodibility, the impact of deglaciation, and mechanisms that control the diversion of the Wutach. Here we present a geomorphometric approach predicated on the stream-power incision model to test these hypotheses. We show that the spatial distribution of knickpoints upstream of the capture is consistent with predictions by the stream-power model. Including proxy information about deglaciation change the parameters of the model, but only slightly increase the fit between modelled and observed knickpoint locations. By comparing estimates of the erodibility derived from knickpoints to those derived from catchment-wide denudation rates in nearby catchments, we observe differences of the order of two magnitudes. The difference in these estimates may can be explained by several processes including channel-bed armouring by sediment. We conclude that stream power parameters derived from the spatial distribution of knickpoints in the Wutach catchment are representative of exceptional (short-term) erosional conditions right after the capture event, rather than of landscape evolution on longer-term geological timescales.
Optimal sensor placement using learning models
Authors: Ćatipović Leon , Kalinić Hrvoje , Matić Frano
Published: March 2022

Measuring data efficiently in the framework of geosciences has proven to be more cumbersome than expected, despite technological advances. While remote sensing techniques, such as satellite observations, provide extraordinary spatial coverage, they still lack the fine spatial and temporal resolution of in situ measuring techniques. Naturally, the level of coverage obtained by remote sensing techniques could be replicated with physical measuring
stations and devices, however, the financial cost would be immense. Therefore, if we are to broaden the spatial coverage while retaining both resolutions and minimising cost, we need to strategically deploy as few sensors as possible. In order to tackle this problem, we have
utilised three unsupervised learning (clustering) methods not only to demonstrate how a smaller subset of sensors can provide significant measurement accuracy, but also to show that there exists an optimal sensor placement (as opposed to random placement). Data used for this
demonstration is ERA5 wind components at 10m height from 1979 to 2019 over the Mediterranean sea, at a spatial resolution of 0.5° × 0.5° every 6 hours.
Clustering methods used are K-means clustering, Self-Organising Maps (SOM) and Growing Neural Gas (GNG). We have clustered the data into 5, 10, 20, 50, 100, 200 and 500 groups and treated the median centers of the resulting domains as the optimal placement for sensors. After the clustering was completed, we have attempted to reconstruct the missing data using two regression models: linear and K-Nearest Neighbours. Reconstructed data was compared (in both size and angle) to original data, and the results show that with just 5 points (out of a grand total of 1244 wet points), reconstruction accuracies are as follows: 65.6, 65 and 62.5% for linear regression reconstruction and 71.6, 71.2 and 70.5% for KNN reconstruction, when applied to GNG, K-means and SOM respectively. Increasing the number of points has diminishing returns (especially in excess of 100 points), with linear regression reconstruction accuracy peaking at ≈ 95% and KNN reconstruction remaining in the high 70%. As demonstrated, GNG and K-means performed slightly better than SOM, due to the nature of SOM’s rigid algorithm.

This work has been supported by Croatian Science Foundation under the project UIP-2019-04-1737.
SeisBench - A Toolbox for Machine Learning in Seismology
Authors: Woollam Jack , Münchmeyer Jannes , Tilmann Frederik , Rietbrock Andreas , Lange Dietrich , Bornstein Thomas , Diehl Tobias , Giunchi Carlo , Haslinger Florian , Jozinovic Dario , Michelini Alberto , Saul Joachim , Soto Hugo
Published: March 2022

Machine Learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large datasets often provides significant improvements over traditional techniques when the number of data are large (>millions of examples). With the entire spectrum of seismological tasks, e.g., seismic picking and detection, magnitude and source property estimation, ground motion prediction, hypocentre determination; among others, now incorporating ML approaches, numerous models are emerging as these techniques are further adopted within seismology.

To evaluate these algorithms, quality controlled benchmark datasets that contain representative class distributions are vital. In addition to this, models require implementation through a common framework to facilitate comparison. Accessing both benchmark datasets, and integrating models built in such varying frameworks is currently a time-consuming process, hindering further advancement of ML techniques within seismology. These development bottlenecks also affect 'practitioners' seeking to deploy the latest models on seismic data, who may not want to necessarily learn entirely new ML frameworks to perform this task.

We present SeisBench as a software package to tackle these issues. SeisBench is an open-source framework for deploying ML in seismology. SeisBench standardises access to both models and datasets, whilst also integrating a range of common processing and data augmentation operations through the API. Through SeisBench, users can access several seismological ML models and benchmark datasets available in the literature via a single interface. SeisBench is built to be extensible, with community involvement encouraged to expand the package. Having such frameworks available for accessing leading ML models forms an essential tool for seismologists seeking to iterate and apply the next generation of ML techniques to seismic data.
Transformations of C, N, P in lotic-lentic transition
Authors: Porcal Petr , Shabarova Tanja
Published: March 2022

The transformation of organic matter in lentic and lotic conditions are dominated by different processes. The special attention deserves the transition from riverine to lacustrine conditions. In our experiment we incubated the stream and pond water inside of mesocosms in the small forest ponds for 28 days. That time was previously observed as sufficient for the reestablishing of chemical and biological processes after severe rain events in the same pond. We aimed to evaluate the dynamics in organic matter, nitrogen, and phosphorus concentrations, fractioning between particulate and dissolved forms, as well as the development of microbial community. Additionally, we tested the influence of presence and absence of solar radiation in one experiment and the effect of different transparency of surrounding environment. The observed changes in C, N, and P fluxes were simulated by the first order kinetics model. The slowest processes were observed in dark mesocosms with the highest initial color and organic carbon content, while the light exposed and less colorful mesocosms revealed fastest rates.
Uniturbulence due to non-linear damping of surface Alfvén waves
Authors: Ismayilli Rajab , Van Doorsselaere Tom
Published: March 2022

We consider a simple 1-D planar equilibrium model with piece-wise constant density. We completed analytical computations in incompressible MHD. First, we derived mathematical formulas for the wave energy density, the rate of energy dissipation, and the energy cascade damping time. Following that, we developed an analytical model to estimate the damping time for the evolution of uniturbulence in surface Alfvén waves. According to the derived equation, the damping time is inversely proportional to the perpendicular wavenumber and the amplitude of the surface Alfvén waves. Next, we determined the numerical energy dissipation rate using the Fourier transform through numerical simulations. Finally, we approximated the damping time using the fundamental mode of a perpendicular wavenumber.
Consequently, we compared our theoretical model to a series of 3D ideal MHD simulations and observed a remarkable resemblance. The numerical findings demonstrate, in particular, that the damping time is inversely related to the density contrast and amplitude of surface Alfvén waves. Besides, we studied third-order structure-function (Yaglom's law) for Uniturbulence. We compared Yaglom's law (predicted energy dissipation) statistics obtained through simulation with our analytical model. In addition, we estimated the inertial range of the turbulent flow.
Springtime stratospheric volcanic aerosol impact on midlatitude cirrus clouds
Authors: Sporre Moa , Friberg Johan , Svenhag Carl , Sourdeval Odran , Storelvmo Trude
Published: March 2022

Explosive volcanic eruptions can reach the stratosphere and cause elevated concentrations of sulfate particles for months to years. When these particles descend into the troposphere they can impact cirrus clouds, though to what degree is unknown. In this study we combine three satellite datasets to investigate the impact of downwelling sulfate aerosol on midlatitude cirrus clouds during springtime. The results show that cirrus clouds in the northern hemisphere (NH) have lower ice water content (IWC), ice crystal number concentrations and cloud fraction (CF) when the aerosol load in the lowermost stratosphere is elevated by volcanism. These changes are largest for the coldest clouds at the highest altitudes. The cirrus clouds in the southern hemisphere on the other hand show no significant changes with downwelling aerosol levels. The reduction in cirrus IWC and CF in the NH imply that volcanic aerosol can cool the climate through reduced warming from cirrus clouds.   
Extracting likely scenarios from high resolution ensemble forecasts in real-time
Authors: Boykin Kris
Published: March 2022

Ensemble forecasts are calculated to give insight into the range of possible future outcomes and potential risks, but it is challenging for operational forecasters to deal with large ensemble data sets and to distil pertinent information from them, especially during high-impact events where forecasts and warnings must be issued and updated quickly with a high degree of accuracy and consistency.  Therefore, it is important to streamline this process by reducing the amount of data an operational forecaster must digest while still maintaining the necessary accuracy.  To do this, a novel clustering technique has been developed for use on ensemble forecasts to extract likely scenarios in real-time.  This technique uses k-medoids clustering and the spatial separation between frontal regions in ensemble members to group similar members together.  Frontal regions are often associated with heavy rain and strong winds, common high-impact events in the UK.  A single representative member is then extracted from each cluster to present to the forecaster as a potential weather scenario.  The method is illustrated with the UK Met Office operation ensemble forecasting system, MOGREPS-G.
Accounting for localization in ensemble network design experiments
Authors: Griewank Philipp , Löhnert Ulrich , Necker Tobias , Nomokonova Tatiana , Weissmann Martin
Published: March 2022

In order to conduct network design experiments for a forecast system, methods are needed to evaluate the potential benefit of hypothetical observations. Ideally these methods are flexible enough to accommodate multiple observation types and forecast lead times, while being computationally fast enough to evaluate many potential observational network layouts. For ensemble forecasts, this can be achieved by assuming a linear sensitivity between the background ensemble perturbations and a forecast quantity of choice. This assumption enables estimating how much the ensemble variance of a chosen forecast quantity would be reduced for an arbitrary combination of observation locations and types, without the need to run additional forecasts. These variance reduction estimates need to take the localization used in the data assimilation framework into account, so that the estimates are consistent with the ensemble forecast system they are derived for. This localization aspect has so far received little attention.

In this presentation we compare two methods to take localization into account when estimating the benefit of hypothetical observations. One method requires inverting the background ensemble covariance matrix. The other method avoids the inversion, but needs to be provided with estimates of signal propagation over time. We use a simple linear advection toymodel to show that while both methods can function well, due to their various strengths and weaknesses they are suited to different applications.