bluuebunny/crossref_metadata_2025 huggingface dataset:
The effect of land use on hydrological processes in the Svratka river basin
Authors: Ghisi Tomáš , Fischer Milan , Bernsteinová Jana , Bohuslav Jakub , Žalud Zdeněk , Zeman Evžen , Trnka Miroslav
Published: February 2023

The Svratka river basin represents an important water resource in the South Moravian Region of the Czech Republic. Due to its relatively low aridity index (the ratio of precipitation to potential evapotranspiration), it belongs to river basins sensitive to climate change. This is also supported by significant negative runoff trends over the past 40 years. The aim of this study is to evaluate the impacts of a hypothetical land use change on the hydrological processes of the Svratka river basin. We used a physically based and spatially distributed hydrological model Mike SHE. The Mike SHE model was calibrated and validated using measured river discharge data in the three hydrological profiles in the Svratka basin for the period 1981–2020. Several land use scenarios were tested against the reference scenario (i.e. current land use) to analyze the impacts of land use change. The land use scenarios encompassed the following hypothetical extreme changes where the entire basin in the model was changed to (i) grassland, (ii) mixed deciduous-coniferous forest, (iii) deciduous forest, or (iv) cropland. These extreme land use scenarios were tested for the baseline period 1981–2020 and also for several CMIP6 downscaled climate models and different socioeconomic pathways (emissions scenarios) up to the end of the 21st century. The results showed that the evapotranspiration was the highest for mixed deciduous-coniferous forest while the lowest was the grassland scenario and cropland . The lowest total runoff from the river basin was simulated for both forest scenarios (mixed forest and deciduous forest). The results also demonstrated that the dominant loss component for all scenarios of the water balance in the Svratka river basin is evapotranspiration. The sensitivity of the hydrological balance in the Czech landscape was also demonstrated, where a slight increase in the evapotranspiration value in the basin has a significant effect on the total runoff from the Svratka basin. The climate change scenarios additionally suggest further exacerbating the water balance and runoff decline in the region. The results of this study are a first step towards evaluating and designing nature-based adaptation measures to climate change in the Svratka river basin. Acknowledgment: The research infrastructure and CzechGlobe team was financially supported by the SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797). The study was also supported by the Internal Grant Agency of the AgriSciences faculty at Mendel University in Brno (AF-IGA2023-IP-031).
Atmospheric Transport Modelling for potential releases and detections of radioxenon possibly connected with nuclear test explosions conducted in North Korea
Authors: Ross J. Ole , Gaebler Peter , Ceranna Lars
Published: February 2023

Various techniques of Atmospheric Transport Modelling were applied after the announced nuclear tests conducted by the DPRK in order to support the analysis of potentially connected radionuclide detections. Forward dispersion forecasts from the test-site predicted potentially affected IMS stations; forward ATM for known background sources assessed their potential contribution to measured concentrations.In case of detections, backward ATM has shown consistency with certain emitter locations and identified coincident source regions for multiple detections.The presentation gives a comprehensive overview how ATM supported the analysis within the German NDC for all six nuclear test explosions announced by the DPRK.  It is particularly in focus how potential interference with known background sources had an impact on the assessment. In several cases, measurements of releases from nuclear facilities caused ambiguous radioxenon detections in the aftermath of DPRK tests.    Finally, for two DPRK tests (2009 and 2016-Sep) it was not possible to identify potentially related radioxenon detections, for two tests there were consistent but not conclusive detections of Xe-133 only  (2016-Jan, 2017) and for two tests there were matching isotopic ratios and fitting atmospheric conditions (2006, 2013) indicating strong evidence for the actual nuclear fission event.
Examining the effect of snow type on effective viscoplastic properties in micro-compression experiments through repeated load-relaxation cycles
Authors: Sundu Kavitha , Ottersberg Rafael , Jaggi Matthias , Löwe Henning
Published: February 2023

It is well known that the snow type can affect the mechanical behavior during slow compression, which may indicate fundamental differences in the deformation mechanisms. To examine these differences, we performed consecutive loading-relaxation tests on three different snow types (rounded grains, depth hoar, and faceted crystals) at the same strain rate of approximately 10-6 s-1 using a micro-compression stage that allowed for X-ray tomography imaging before and after the experiment. By using consecutive loading-relaxation cycles, we were able to eliminate unavoidable structural transients that occurr during the first loading. This allowed us to study the stress-time data in the following cycles and probe the pure viscoplastic behavior of the intact ice matrix in the snow in the absence of microstructural changes. We could consistently analyze the stress-time data of all curves using an implicit, analytical solution of a non-linear Maxwell model for loading and relaxation. Our analysis showed that the estimated mechanical parameters were highly consistent between loading and relaxation and between consecutive cycles. We observed that the exponent n in Glen's law takes either high or low values depending on snow type: rounded grains with n=1.9 and depth hoar/faceted crystals with n=4.4. The transition from rounded grains to depth hoar/faceted crystals also appears consistent with an underlying influence of the optical equivalent diameter but clearly rules out a previously hypothesized dependence of n on volume fraction. In contrast, the effective compactive viscosity obtained from loading and relaxation had a dependency on volume fraction. Our results complement the understanding of how snow type and microstructure influence the mechanical behavior during slow compression, which we discuss in terms of potential transitions in dominant deformation mechanisms.
Model-based assessment of dynamic volume estimates for karst aquifers
Authors: Birk Steffen , Abirifard Mahmoud
Published: February 2023

Karst springs frequently drain large catchment areas and thus represent important water resources. Adequate management of karst water resources requires quantitative information about the drainable water volume, i.e., the dynamic volume of the karst aquifer supplying the spring. The mathematical integration of a functional relationship fitted to the observed discharge recession curve is one approach commonly employed for this purpose. Yet, this approach implicitly assumes that the observed recession behavior can be extrapolated to longer times and lower discharge values. Here, we explore the adequacy of this approach using the numerical karst groundwater flow model MODFLOW-CFP to simulate the discharge recession of hypothetical karst aquifers. While the model scenarios represent simplified hydrogeological settings, each of them includes complexities that may be encountered in real karst aquifers. By comparing the actual dynamic volume of the modelled aquifer to the volume estimate obtained from recession analysis, we identify factors potentially affecting the accuracy of the dynamic volume estimate (Abirifard et al., J. Hydrol., 2022, https://doi.org/10.1016/j.jhydrol.2022.128286 ). It is found, for example, that a decrease of hydraulic conductivity with depth causes underestimation of the dynamic volume, whereas groundwater abstraction within the spring catchment results in an overestimation. Real-world examples where these factors likely affect the recession behavior and thus the dynamic volume estimate are identified and described.
A multi-centennial mode of North Atlantic climate variability throughout the Last Glacial Maximum
Authors: Prange Matthias , Jonkers Lukas , Merkel Ute , Schulz Michael , Bakker Pepijn
Published: February 2023

Paleoclimate proxy records from the North Atlantic region reveal substantially greater multi-centennial temperature variability during the Last Glacial Maximum (LGM) compared to the current interglacial. As there was no obvious change in external forcing, causes for the increased variability remain unknown. Here we provide a mechanism for enhanced multi-centennial North Atlantic climate variability during the LGM based on experiments with the coupled climate model CESM. The model simulates an internal mode of multi-centennial variability, which is associated with variations in the Atlantic meridional overturning circulation. In accordance with high-resolution proxy records from the glacial North Atlantic, this mode induces highest surface temperature variability in subpolar and mid latitudes and almost no variance in low latitudes. Greenland surface air temperature varies by up to 4°C, which is in line with multi-centennial variability reconstructed from ice cores. We show that this mode is based on a salt-oscillator mechanism and emerges only under full LGM climate forcing. Moderate deviations from full-glacial boundary conditions lead to its disappearance. We further argue that the multi-centennial mode has to be distinguished from millennial-scale Dansgaard-Oeschger oscillations.
Earth observation time series for the monitoring of droughts in Cyprus: Patterns and drivers of vegetation dynamics
Authors: Uereyen Soner , Eisfelder Christina , Gessner Ursula , Reinermann Sophie , Asam Sarah , Panagiotou Constantinos F. , Eliades Marinos , Varvaris Ioannis , Loulli Eleni , Pittaki Zampela , Hadjimitsis Diofantos , Kuenzer Claudia , Bachofer Felix
Published: February 2023

With amplified climate warming, climate extremes over Europe become more frequent. Since the 2000’s, many years have been characterized by extreme events such as droughts and heat waves. For example, in Central Europe, extreme droughts and heat waves took place in the years 2003 and 2018. In comparison, Cyprus experienced strong droughts during 2003 and 2016-2018. Such extreme climate events can have severe impacts on agricultural yields, the productivity of natural vegetation, and on water resources. In this regard, long-term Earth observation (EO) time series are essential to quantitatively assess and analyse changes on the land surface, including vegetation condition. In this study, a joint analysis of geoscientific time series over the last two decades, including EO-based MODIS vegetation indices and meteorological variables is performed to assess drought events and analyse trends as well as potential drivers of vegetation dynamics in Cyprus. The analysis of drought events and vegetation trends is based on the full archive of MODIS imagery at 250 m spatial resolution covering the period 2000-2022. In detail, climate-related effects on vegetation were analysed by means of the deviations of MODIS 16-day vegetation index composites from their long-term mean. Next, trends of the MODIS vegetation index were calculated to evaluate spatial patterns of vegetation change over the investigated period. These analyses were additionally performed for geographically stratified regions, including diverse vegetation classes such as cropland and grassland. Furthermore, the application of a causal discovery algorithm reveals linkages within a multivariate feature space, in particular between vegetation greenness and climatic drivers. Preliminary analyses showed that drought patterns differ with respect to seasons and the investigated vegetation class. For example, the strong drought year 2008 is clearly reflected in the results, whereas forest areas appear to be least affected by the drought during the spring months. Moreover, considering the significant trends over the last two decades, an increase in vegetation greenness could be observed.
Simulating space-borne atom interferometers for Earth Observation and tests of General Relativity
Authors: Struckmann Christian , Rasel Ernst M. , Wolf Peter , Gaaloul Naceur
Published: February 2023

Quantum sensors based on the interference of matter waves provide an exceptional performance to test the postulates of General Relativity by comparing the free-fall acceleration of matter waves of different composition. Space-borne quantum tests of the universality of free fall (UFF) promise to exploit the full potential of these sensors due to long free-fall times, and to reach unprecedented sensitivity beyond current limits.
In this contribution, we present a simulator for satellite-based atom interferometry and demonstrate its functionality in designing the STE-QUEST mission scenario, a satellite test of the UFF with ultra-cold atoms to 10^-17 as proposed to the ESA Medium mission frame [https://arxiv.org/abs/2211.15412]. Moreover, we will highlight the possibility of this simulator to design Earth Observation missions going beyond state of the art such as the CARIOQA concept [https://arxiv.org/abs/2211.01215].
This work is supported by DLR funds from the BMWi (50WM2263A-CARIOQA-GE and 50WM2253A-(AI)^2).
Icy thermometers: how volcanic heat affects glacier mass balance
Authors: Spagnolo Matteo , Howcutt Stephen , Rea Brice , Jaszewski Jan , Barr Iestyn , Coppola Diego , de Siena Luca , Girona Társilo , Gomez-Patron Andie , Mullan Donal , Pritchard Matthew
Published: February 2023

  With more than 30 million people living within 10 km of active or dormant volcanoes, eruptions are a natural socio-economic hazard that can have devastating consequences for society. Hence, the timely forecasting of volcanic unrest has real-world, and potentially, life and death implications. A major challenge is the identification and monitoring of precursors to forthcoming volcanic eruptions. The observation and measurement of thermal anomalies is one of the answer to this challenge, with some volcanoes exhibiting signs of thermal unrest over extensive areas of their edifice for several years prior to an eruptive event. Glaciers that sits on volcanoes are likely to respond to the increased heat and could therefore be used as complementary volcano thermometers but a large scale study is missing. Our study, which covers 600 Andean glaciers and 37 ice-clad volcanoes, demonstrate glacier mass balance sensitivity to volcanic heat. We distinguish between ‘volcanic-glaciers’ (located ≤1 km from volcanic centres), and ‘proximal glaciers’ (1-15 km) and calculate their equilibrium line altitude (ELA). In most instances, proximal glacier ELAs are lower than those of nearby volcanic-glaciers. In some cases, the ELA decrease proportionally with increasing glacier distance from the volcanic edifice, and a quantitative relationship between ΔELAmean (i.e., the difference in mean ELA between the proximal and volcanic-glaciers) and ASTER-based measurements of volcanic thermal anomalies could be established. These results highlight the impact of volcanic heat on glacier mass balance; emphasise the need to exclude volcanic-glaciers from glacier-climate investigations; and demonstrate the first-order potential of glaciers as ‘volcanic thermometers’, with the ΔELAmean representing a proxy for volcanic heat.
The salt-bearing rifted margins in West Africa – regional structural variability and salt tectonics between Gabon and Namibia
Authors: Callot Jean-Paul , Pichel Leonardo , Legeay Etienne , Ringenbach Jean-Claude
Published: February 2023

Salt-bearing rifted margins comprise some of the most structurally complex and economically important sedimentary basin settings such as the South Atlantic and the Gulf of Mexico salt basins. They are also involved with some of the largest uncertainties regarding the crustal and syn-rift basin architecture and supra-salt tectonic evolution, as well as the link between rifted margin architecture with salt deposition and post-rift gravity-driven salt tectonics. We thus conduct a margin-scale study along nearly the entire West Africa salt basin, from south Gabon to Namibe, combining a vast dataset of 2D and 3D seismic and well data with gravimetric and magnetic data to analyse its along-strike rift and salt tectonics structural variability. We construct regional structural and thickness maps of key salt and post-salt intervals to depict the history of individual margin segments and to investigate: 1) how rifting and rifted margin architecture influences post-rift salt tectonics evolution, 2) how these vary through time and space, and 3) what are the controls between their different salt tectonics structural styles. We show that rifting and rift structures controlled the salt basin geometry, thickness, and base-salt relief in different ways for the different margin segments, and drastically influenced their post-rift salt tectonic evolution. Differences in post-salt sediment supply and continental uplift also had a role on their evolution. The results also have implications to understand the interplay between rifted margin architecture with post-rift salt tectonics for worldwide salt-bearing margins.
Spatial response of Greenland’s firn layer to NAO variability
Authors: Brils Max , Kuipers Munneke Peter , van den Broeke Michiel
Published: February 2023

Changes in the Greenland ice sheet (GrIS) firn layer may impact its ability to retain meltwater. These changes also need to be accounted for when converting measured ice sheet volume changes to mass changes. With a firn model (IMAU-FDM v1.2G) forced by a regional climate model (RACMO2.3p2), we investigate how the GrIS firn layer depth and pore space have evolved since 1958 in response to variability in the large-scale atmospheric circulation. On interannual timescales, the firn layer’s depth and pore space shows a spatially heterogeneous response to variability in the North Atlantic Oscillation (NAO). Notably, a stronger NAO following the record warm summer of 2012 led the firn layer in the south and east of the ice sheet to regain thickness and pore space after a period of thinning and reduced pore space. The main driving forces behind these changes vary between GrIS sectors: in the southwest, a decrease in melt dominates, whereas in the east an increase in snow accumulation dominates. However, these trends are not uniform across the GrIS, and over the same period, the firn in the northwest continued to lose pore space. The NAO is also stronger in winter than in summer and we observe that this impacts the seasonal cycle of the firn. In the wet southeastern GrIS, most of the snow accumulates during the winter, when firn compaction is slow, amplifying the seasonal cycle in firn depth and pore space. The opposite occurs in other regions, where snowfall peaks in summer or autumn, at the same time as densification and melt, damping the seasonal oscillations in the firn thickness and pore space.
Advances in landslide analysis by using remote sensing and artificial intelligence (AI): Results from MultiSat4SLOWS project
Authors: Motagh Mahdi , Plank Simon , Wang Wandi , Orynbaikyzy Aiym , Vassileva Magdalena , Sips Mike
Published: February 2023

Landslides are a major type of natural hazard that cause significant human and economic losses in mountainous regions worldwide. Optical and synthetic aperture radar (SAR) satellite data are increasingly being used to support landslide investigation due to their multi-spectral and textural characteristics, multi-temporal revisit rates, and large area coverage. Understanding landslide occurrence, kinematics and correlation to external triggering factors is essential for landslide hazard assessment. Landslides are usually triggered by rainfall and thus, are often covered by clouds, which limits the use of optical images only. Exploiting SAR data, and their cloud penetration and all weather measurement capability, provides more precise temporal characterization of landslide kinematics and its occurrence. However, except for a few research studies, the full potential of SAR data for operational landslide analysis are not fully exploited yet. This is a very demanding task, considering the availability of a vast amount of Sentinel-1 data that have been globally available since October 2014.In this presentation we summarise all the achievements that were made within the framework of MultiSat4SLOWS project (Multi-Satellite imaging for Space-based Landslide Occurrence and Warning Service), financed within the Helmholtz Imaging 2020 call. The project aims on developing a multi-sensor approach for detection and analysis of the landslide occurrence time and its spatial extent using freely available SAR data from Sentinel-1. Within this project,  we generated a reference database based on Sentinel-1 and -2 data for training, testing and validation of deep learning algorithms. The reference database contains various landslide examples that occurred worldwide and include pre- and post-event polarimetric, coherence and backscatter features. Also, we investigated the applicability of SAR/InSAR time-series data for landslide time detection. Finally, we introduce a prototype of a Visual Analytics platform for rapid landslide analysis of spatial and temporal ground deformation patterns and correlation with external triggering factors. 
Monitoring spatial and temporal carbon dynamics in the plant soil system by co-registration of Magnetic Resonance Imaging and Positron Emission Tomography for image guided sampling
Authors: Koller Robert , Huber Gregor , Pflugfelder Daniel , van Dusschoten Dagmar , Hinz Carsten , Schultes Sina , Chlubek Antonia , Knief Claudia , Metzner Ralf
Published: February 2023

Individual plants vary in their ability to respond to environmental changes. The plastic response of a plant enhances its ability to avoid environmental constraints, and hence supports growth, reproduction, and evolutionary and agricultural success.Major progress in the analysis of above- and belowground processes on individual plants has been made by the application of non-invasive imaging methods including Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET).MRI allows for repetitive measurements of roots growing in soil and facilitates quantification of root system architecture traits in 3D. PET, on the other hand, opens a door to analyze dynamic physiological processes in plants such as long-distance carbon transport in a repeatable manner. Combining MRI with PET enables monitoring of short livedCarbon tracer (11C) allocation along the transport paths (i.e. roots visualized by MRI) into active sink structures.To analyse the link between root-internal C allocation patterns and C metabolism in the rhizosphere, we are combining 11CO2 with stable 13CO2 labelling of plants. Isotope ratio mass spectrometry (IRMS) analyses of rhizosphere soil is applied to link root-internal C allocation patterns with distribution of 13C in the rhizosphere soil. The metabolically active rhizosphere organisms are subsequently identified based on DNA 13C stable isotope probing.In our presentation we will highlight our approaches for gathering quantitative data from both image-based technologies in combination with destructive analysis that provides insights into the functioning and dynamics of C transport processes in the plant-soil system.
Relative earthquake relocations and detailed evolution of failed and successful lateral dyke intrusions during the 2021-2022 Fagradalsfjall volcano-tectonic rifting event
Authors: Ágústsdóttir Thorbjörg , Gudnason Egill Árni , Magnússon Rögnvaldur Líndal , Fischer Tomáš , Winder Tom , Eibl Eva P. S. , Glastonbury-Southern Esme , Hersir Gylfi Páll , Horálek Josef , Doubravová Jana , Vlček Josef , Hrubcová Pavla , Málek Jiri , Fojtíková Lucia , Brandsdóttir Bryndís
Published: February 2023

The 6-month long fissure eruption that started in Geldingadalir valley within Mt. Fagradalsfjall, Reykjanes Peninsula, SW Iceland, on 19March 2021 was preceded by three weeks of intense seismic activity associated with a ~10 km long NE-SW oriented dyke intrusion, along the Fagradalsfjall volcanic system. This was the first eruption in over 800 years on the Peninsula. A multi-institutional seismic network, installed prior to the dyke intrusion, comprises 27, 3-component instruments (25 broadband and 2 short-period instruments) covering the whole Reykjanes Peninsula. Here we focus on the Fagradalsfjall area (~12x10 km) with 4 instruments located within a 2.5 km radius of the observed dyke seismicity. Accurate automatic earthquake locations using a new detection and location algorithm QuakeMigrate[1] obtain an order of magnitude higher number of earthquakes than conventional location methods. For high precision locations, events are cross-correlated and then relatively relocated using GrowClust[2]. Here we present detailed earthquake location results from 18 September 2021 to 30 September 2022. This period comprises i) the 2021 post-eruptive seismicity along the 10 km long 2021 dyke path; ii) an earthquake swarm about 5 km NE of the eruption site at 5-7 km depth in October; iii) a 5 day-long dyke intrusion in December 2021 that failed to breach the surface; iv) a 5-day-long dyke intrusion that breached the surface on 3 August 2022, and led to a 6 week-long fissure eruption in Meradalir, located about 0.5 km NE of the 2021 eruption site.We find that the failed dyke in December 2021 and the 2022 dyke that successfully breached the surface share many of the same features. They both propagated at similar depths of 3-6 km, in the pathway of the initial 2021 dyke and both show some sparser seismicity closer to the surface. The time span of their propagation is almost identical; both are propagating for around 5 days, with similar lengths of about 6 km, which is considerably shorter than the 10 km long 3-week 2021 dyke propagation. They differ, however, in their location with respect to the 2021 eruption site. The failed 2021 dyke intrusion propagated mainly SW of the 2021 eruption site, whereas the successful 2022 dyke propagated NE of it. Interestingly, our results suggest that during the initial phases of the 2022 dyke intrusion, two dykelets propagate in opposite directions simultaneously.
Nodal Ambient Noise Tomography and automatic picking of dispersion curves with convolutional neural network: case study at Vulcano-Lipari, Italy
Authors: Stumpp Douglas , Amir Jiwani-Brown Elliot , Barat Célia , Lupi Matteo , Muñoz Francisco , Planes Thomas , Savard Geneviève
Published: February 2023

The ambient noise tomography (ANT) method is widely adopted to reconstruct shear-wave velocity anomalies and to generate high-resolution images of the crust and upper-mantle. A critical step in this process is the extraction of surface-wave dispersion curves from cross-correlation functions of continuous ambient noise recordings, which is traditionally performed manually on the dispersion spectrograms through human-machine interfaces. Picking of dispersion curves is sometimes prone to bias due to human interpretation. Furthermore, it is a laborious and time-consuming task that needs to be resolved in an automatized manner, especially when dealing with dense seismic network of nodal geophones where the large amount of generated data severely hinders manual picking approaches. In the last decade, several studies successfully employed machine learning methods in Earth Sciences and across many seismological applications. Early studies have shown versatile and reliable solutions by treating dispersion curve extraction as a visual recognition problem. We review and adapt a specific machine learning approach, deep convolutional neural networks, for use on dispersion spectrograms generated with the usual frequency-time analysis (FTAN) processing on ambient noise cross-correlations. To train and calibrate the algorithm we use several available datasets acquired from previous experiments across different geological settings. The main dataset consists of records acquired with a dense local geophone network (150 short period stations sampling at 250 Hz) deployed for one month in October 2021. The dataset has been acquired during the volcanic unrest of the Vulcano-Lipari complex, Italy. The network also accounts for additional 17 permanent broadband stations (sampling at 100 Hz) maintained by the National Institute of Geophysics and Volcanology (INGV) in Italy. We evaluate the performance of the dispersion curves extraction algorithm. The automatically-picked dispersion curves will be used to construct a shear-wave velocity model of the Vulcano-Lipari magmatic plumbing system and the surrounding area of the Aeolian archipelago. 
Centennial to millennial climate variability across climate states; proxy reconstructions vs. transient model simulations.
Authors: Dolman Andrew M. , Kapsch Marie , Mikolajewicz Uwe , Jonkers Lukas , Laepple Thomas
Published: February 2023

In previous model-data comparisons, the centennial to millennial scale variance of local climate (e.g., SST) reconstructed from proxies was significantly higher than that simulated by climate models. One possible explanation is the lack of long-term feedback mechanisms, e.g. from the representation of changes in ice-sheets in models. Additionally, proxy records are short, and sparse, and the climate signal is significantly modified during the processes of encoding, archiving, and recovery.Here we introduce new methods to infer the climate variability of the past from proxy data and compare them to new transient model simulations of the last deglaciation. This will allow us to estimate the amplitude of climate variability and to evaluate whether climate models are capable of capturing changes in climate variability between different climate states (e.g. glacial vs. interglacial periods), which is also relevant for the accuracy of future projections. We compare the variability of marine d18O reconstructed from proxies with that simulated by a state-of-the-art Earth System Model.From the proxy side, our analysis is based on a new dataset of marine oxygen isotope data from planktonic foraminifera compiled for the PALMOD project. We use new methods to first calculate power-spectra for the LGM, transition and Holocene and then to correct these spectra by fitting a Bayesian model describing the effects of bioturbation and measurement error on the reconstructed climate signal. From the model side we use marine d18O variability calculated using temperature and salinity from transient model simulations of the last deglaciation, performed within the PALMOD project, that include changes in the ice sheets.This combination of new data and methods will allow us to investigate the effect of different ice-sheet configurations and physical parametrizations in the model on their ability to characterise long-timescale climate variability and its dependence on climate state.
Towards a machine learning based multimodel for precipitation forecast over the italian peninsula
Authors: Monaco Luca , Cremonini Roberto , Laio Francesco
Published: February 2023

Direct model output forecasts by Numerical Weather Prediction models (NWPs) present some limitations caused by errors mostly due to sensitivity to initial conditions, sensitivity to boundary conditions and deficiencies in parametrization schemes (i.e. orography).These sources of error are unavoidable, and atmospheric chaotic dynamics make prediction errors spread rapidly in time in the course of the forecast, inducing both systematic and random errors.Nonetheless, in the last 50 years, NWPs had a significant decrease in the impact of these sources of errors, even in the long-term forecast, thanks for instance to an ever-increasing computational capability, but their relevance is still not neglectable.Moreover, different NWPs present specific different pros and cons which are findable empirically. For instance, in the case of precipitation forecast in north-west Italy, low-resolution models (e.g. ECMWF-IFS) are more reliable in terms of space and time in predicting the average precipitation, while high-resolution models (e.g. COSMO-2I) tend to forecast better the maximum precipitation. Research purposes apart, actual limitations must be seen in an operational context, where weather forecasts’ skillfulness and associated uncertainty are information of the utmost importance to the forecaster and in general to the user of a certain forecasts system.To tackle these limitations of NWPs and the need for an uncertainty-quantified meteorological forecast, we propose a machine learning-based multimodel post-processing technique for precipitation forecast. We focus on precipitation since it is the most important variable in the issue of spatially localized weather alert notice by the Italian Civil Protection system and at the same time it is one of the most challenging variables to forecast. We use a Convolutional Neural Network (CNN) to obtain deterministic and probabilistic forecast grids over 24h up to 48h focusing on North-West Italy, using several high and low-resolution deterministic NWPs as input and using high-resolution rain-gauge corrected radar observations for the training. The effect of the usage of different convolutional parameters (e.g. stride, padding) is taken into account. The deterministic output grid is chosen as the grid with the lowest mean square error obtained during the training, and it is compared with the linear regression of the input NWPs and with every single model. The probabilistic output grid is generated by considering the statistical ensemble of the twenty grids with the lowest mean square error obtained during the training, and it is compared with the logistic regression of the input NWPs and with ECMWF-EPS as a benchmark, both at different precipitation thresholds.
Assessing daily precipitation tails over India under changing climate
Authors: Gupta Neha , Chavan Sagar
Published: February 2023

Daily precipitation extremes are crucial in the hydrological design of major water control structures. The extremes are usually present in the upper part of the probability distribution of daily precipitation data, known as the tail. The distributions are bifurcated into heavy or light-tailed distributions depending on the tail. Heavy tails signify a higher frequency of occurrences of extreme precipitation events. Prediction of extreme precipitation depends on reliable modelling of the tail. Tail behaviour can be studied by graphical as well as threshold-based fitting approaches; however, each approach has associated shortcomings. In this work, we utilize a versatile and simple empirical index known as the “Obesity Index” (OB) to assess the tail of probability distributions of daily gridded precipitation data for India. This comprehensive regional analysis has been undertaken to quantify the tail heaviness of 4801 daily precipitation records over India for historical (1970–2019) and future (2020–2100) time periods. Future projections of daily precipitation are downscaled from the latest generation of climate models knowns as Coupled Model Intercomparison Project Phase 6 (CMIP6) under different emission scenarios. Finally, the application of the OB-based approach is extended to characterize daily precipitation in Indian Meteorological Subdivisions. Results indicate the applicability of heavy-tailed distributions in representing daily precipitation over India and establish the utility of the OB-based approach in diagnosing tail behaviour. Also, the spatial patterns of the tail heaviness are found to be matching with the Köppen–Geiger climate classification of India. The findings from this can be an input for the policymakers to develop adaptation strategies in response to the projected climate change impact.Keywords: Extreme precipitation, Climate Change, India, Obesity index, Tail heaviness 
Measuring bedload motion time at sub-second scale using Benford's law from long-term acoustic recordings
Authors: Yang Ci-Jian , Turowski Jens.M , Zhou Qi , Tang Hui , Nativ Ron , Chen Wen-Sheng
Published: February 2023

Bedload transport is a natural process that strongly affects the Earth’s surface system. An important component of quantifying bedload transport and establishing early warning systems is obtaining the parameters at the onset of bedload motion. Bedload transport can be monitored with passive acoustic methods, e.g., hydrophones. Yet, an efficient method for identifying the onset of bedload transport from long-term continuous acoustic data is still lacking. Benford’s Law defines the specific frequency distribution of the first digits of datasets that have been used to distinguish stochastic from chaotic processes in nature when this process causes higher energy events than baseline. Here, we apply Benford’s law to continuous acoustic recordings from Baiyang hydrometric station, a tributary of Liwu River, Taiwan at the frequency of 32 kHz from stationary hydrophones deployed for three years since 2019. We construct a workflow to parse sound combinations of bedload transportation and analyze them in the context of hydrometric sensing constraining the onset, and recession of bedload transportation. We identify two bedload transportation events that lasted 17 and 45 hours, respectively, covering about 0.35% of the time per year. Our workflow allows filtering 99% of background signal and focuses on two events including bedload motions. Given that fluvial seismology has successfully monitored fluvial processes, continuous monitoring in three directions (N-S, W-E, vertical) brings board discussion orientations, e.g., the direction of source or migration of mass movement. Therefore, we suggest that the application of Benford’s law on seismic data of Earth's surface processes has great potential.
Using Causal Discovery to Clarify Observed and Simulated Relationships Between ENSO and Other Ocean Basins
Authors: Herman Rebecca , Runge Jakob
Published: February 2023

Observed sea-surface temperatures in various ocean basins are confounded by anthropogenic and natural radiative forcing and by teleconnections to modes of internal variability, especially the El Nino Southern Oscillation (ENSO). While confounding due to anthropogenic and natural forcing can be removed in coupled simulations, confounding due to ENSO is unavoidable. When not appropriately characterized and quantified, this confounding can obscure causal relationships between various ocean basins and atmospheric phenomena of huge humanitarian import, such as monsoon rainfall, with implications for attribution of past disasters and prediction of the future. These relationships have been difficult to characterize in part because observational data is limited and simulated data may not represent the observed climate system. This study uses causal discovery to examine the coupled relationships between ENSO and other ocean basins in simulations and observations. We begin by evaluating the (L)PCMCI(+) causal discovery algorithms under various conditions and assumptions on data generated by two continuous idealized models of ENSO: the classic Zebiak-Cane model and a simple stochastic dynamical model proposed by Thual, Majda, Chen, and Stechmann. We then apply the causal discovery algorithms to seasonally and spatially-averaged sea surface temperature (SST) indices for ENSO and other ocean basins in preindustrial control simulations from the Coupled Model Intercomparison Project Phase 6. We discuss the robustness of the results, and the differences between the causal relationships in different General Circulation Models. Finally, we apply the causal learning algorithm to observed SST, and discuss to what extent simulated relationships can be used to learn about the observed climate system. We additionally demonstrate the implications of this study for other scientific questions, specifically for understanding variability in Sahel Monsoon rainfall.
Combining thermochronological data with 3D probabilistic kinematic modeling of the Bavarian Subalpine Molasse for uncertainty estimation
Authors: Brisson Sofia , Ziegler Josefine , Chudalla Nils , Wellmann Florian , von Hagke Christoph
Published: February 2023

Thermokinematic modeling often relies on prescribed geometric and kinematic models at depth without considering their uncertainty. This does not allow for the proper quantification of the relative contributions of different drivers to the exhumation signal. Considering uncertainty of structural data in thermokinematic models would help understanding how much shortening associated with the observed cooling signal occurred. The aim of this work is to combine probabilistic structural modeling with thermokinematic forward simulations to investigate the related uncertainties. For this purpose, the Bavarian Subalpine Molasse is particularly suited as a test case, as it connects the Alpine orogen with its foreland, and should shed light on the strain distributions during the latest stages of Alpine mountain building. 3D implicit geological modeling of the Bavarian Subalpine Molasse triangle zone was carried out and combined with a systematic random sampling approach to automatically generate an ensemble of geometric models in the range of assigned uncertainties. In addition, a probabilistic 3D kinematic forward model is constructed. A link can then be obtained between kinematic model parameters and present-day geometry in comparison with field observations at the surface and also in comparison to the range of geometric uncertainties in the 3D geological model. Results show that the uncertainty (quantified as information entropy) is distributed as a function of structural complexity, depth, and data density throughout the geometric model, and additionally where fault slip ranges are large in the kinematic model.In a next step, these models are combined with a thermokinematic forward model to integrate thermochronological measurements from previous campaigns, and eventually own measurements, to obtain an integrated picture of foreland evolution and associated uncertainties over space and time.
The Application of Informational Predictability to Rainfall Data
Authors: Carsteanu Alin-Andrei , Fernández Méndez Félix
Published: February 2023

Informational predictability, as defined in Fernández Méndez et al. [Stoch. Environ. Res. Risk Assess. (2023), submitted for publication] is based on the normalized complement of the expected value of the logarithm of the conditional probability, to be precise, this refers to the probability of the predicted events, when conditioned upon their respective predictors. The present work focuses on balancing the precision of the prediction, as measured by the narrowness of the predicted intervals, against the respective probabilities of a correct prediction, which finally amounts to maximizing the informational predictability. The data are high-resolution temporal rainfall intensity series, measured by an optical rain gauge.
Transparent and reproducible data analysis workflows in Earth System Modelling combining interactive notebooks and semantic data management
Authors: Schlemmer Alexander , Lennartz Sinikka
Published: February 2023

In our project we are employing semantic data management with the Open Source research data management system (RDMS) CaosDB [1] to link empirical data and simulation output from Earth System Models [2]. The combined management of these data structures allows us to perform complex queries and facilitates the integration of data and meta data into data analysis workflows.One particular challenge for analyses of model output is to keep track of all necessary meta data of each simulation during the whole digital workflow. Especially for open science approaches it is of great importance to properly document - in human- and computer-readable form - all the information necessary to completely reproduce obtained results. Furthermore, we want to be able to feed all relevant data from data analysis back into our data management system, so that we are able to perform complex queries also on data sets and parameters stemming from data analysis workflows.A specific aim of this project is to re-analyse existing sets of simulations under different research questions. This endeavour can become very time consuming without proper documentation in an RDMS.We implemented a workflow, combining semantic research data management with CaosDB and Jupyter notebooks, that keeps track of data loaded into an analysis workspace. Procedures are provided that create snapshots of specific states of the analysis. These snapshots can automatically be interpreted by the CaosDB crawler that is able to insert and update records in the system accordingly. The snapshots include links to the input data, parameter information, the source code and results and therefore provide a high-level interface to the full chain of data processing, from empirical and simulated raw data to the results. For example, input parameters of complex Earth System Models can be extracted automatically and related to model performance. In our use case, not only automated analyses are feasible, but also interactive approaches are supported.[1] Fitschen, T.; Schlemmer, A.; Hornung, D.; tom Wörden, H.; Parlitz, U.; Luther, S. CaosDB—Research Data Management for Complex, Changing, and Automated Research Workflows. Data 2019, 4, 83. https://doi.org/10.3390/data4020083
[2] Schlemmer, A., Merder, J., Dittmar, T., Feudel, U., Blasius, B., Luther, S., Parlitz, U., Freund, J., and Lennartz, S. T.: Implementing semantic data management for bridging empirical and simulative approaches in marine biogeochemistry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11766, https://doi.org/10.5194/egusphere-egu22-11766, 2022.
Investigating diurnal ecosystem scale H2O and CO2 isotope fluxes in an irrigated semi-arid environment during the LIAISE 2021 field campaign
Authors: Moonen Robbert , Adnew Getachew , Hartogensis Oscar , Vila-Guerau de Arellano Jordi , Röckmann Thomas
Published: February 2023

Validation of gas exchange fluxes in models has been challenging due to the lack of ecosystem scale exchange fluxes partitioned into soil, plant and atmospheric components. One promising method to partition turbulent fluxes uses the exchange process dependent fractionation of molecules like CO2 and H2O. When applying this method to short spatiotemporal scales, an isotope flux (δ-flux) needs to be resolved. Few have attempted to measure this δ-flux as the required instrumentation only became available in recent years. In our presentation we will discuss observations made during the LIAISE 2021 field campaign using an EC system, Picarro L-2130i H2O isotope analyser, and Aerodyne TILDAS-CS CO2 isotope analyser. This campaign took place in the summer of 2021 in the heavily irrigated Ebro River basin near Lleida, Spain embedded in a semi-arid region.We will present procedures to estimate and scrutinize the central δ-flux variable. To this end we calculated co-spectra of the relevant signals and compared their frequency dependent contributions. One relevant finding is that mole fractions and isotope ratios measured with the same instrument can be offset in time by more than a minute, thereby impacting the resulting δ-fluxes. Additionally, we found asymmetric signal loss between net ecosystem fluxes and δ-fluxes. We will show that such effects impact flux partitioning severely and indicate how they can be tackled using physically sound corrections. Only when such corrections and verifications are made, ecosystem flux partitioning can be applied to validate conceptual land-atmosphere exchange models. Such models will calculate the diurnal variability of CO2 and H2O isotopologue concentrations, and link local to regional scales, all with the purpose of better constraining current and future exchange fluxes. 