bluuebunny/crossref_metadata_2025 huggingface dataset:
Moving from disaster reports to disaster tales to increase flood risk awareness
Authors: Mazzoglio Paola , Macchia Stefano , Gallo Enrico , Winter Julia , Claps Pierluigi
Published: February 2023

The lack of basic information about the area where people live and about major hydrometeorological disasters that occurred in the past can lead the population to underestimate the flood risk. The flood risk perception is often related only to past direct experiences and to the memory of floods that took place in the same areas. However, this risk perception is high during the weeks that follow a flood event and then decreases rapidly.In Italy, the regional agencies in charge of flood management widely use disaster reports (DRs) as the preferred way to collect and standardize information on past flooding events and on the meteorological conditions of that events. However, these DRs are often examined only by technicians, while the general audience is rarely aware about their availability or cannot fully understand what is reported in these documents.In this work, we performed a systematic survey of the DRs drawn in the past years by the Italian regional agencies in charge of civil protection. We suggest that these DRs could be improved to provide more effective communication to citizens in view of increasing flood risk awareness, reinforcing the communication of civil protection planning and management, and improving the resilience of the population to extreme events. More specifically, we suggest that these disaster reports could be reframed into disaster tales (DTs) without losing the detail level required for the typical technical uses of the DRs. Moreover, these recompiled DTs can be used as tools that offer wider knowledge of the events to improve people’s preparedness and self-protection behavior when a future major flood event will occur.From a practical point of view, we suggest improving the structure of these reports with the integration of short videos and pictures taken by citizens during the event, maps and interactive tools able to present handier multimedia views of the events. By watching and listening to what has happened, the population can better understand the feelings of the people experiencing an emergency, learning how to act during future floods. We also suggest using a storyline approach to present the whole sequence of events and decisions taken during the flood, putting in chronological order the most significant episodes occurred during the event and the recovery phase.Application to some case studies of flooding occurred in Italy illustrates how to implement the DRs to create more readily accessible DTs.
Waveform retracking based on a Convolutional Neural Network applied to Cryosat-2 altimeter data
Authors: Dehghanpour Alireza , Helm Veit , Humburt Angelika , Hänsch Ronny , Horwath Martin
Published: February 2023

The Antarctic Ice Sheet is an important indicator of climate change and a major contributor to sea level rise. Hence, precise, long-term observations of surface elevation change are required to assess changes and their contribution to sea level rise. Satellite altimetry has been used by various missions to measure surface elevation change since 1992. It has been shown that, next to the surface slope and complex topography, one of the most challenging issues is the spatial and temporal variability of radar pulse penetration into the snowpack, especially over the vast East Antarctic plateau. This results in an inaccurate measurement of the true surface elevation and consequently affects surface elevation change (SEC) estimates.To increase the accuracy and correct the SEC, we developed a deep convolutional neural network (CNN) architecture. The CNN was trained by a simulated waveform data set containing more than 3.6 million waveforms, considering different surface slopes, topography, and attenuation. The CNN follows standard architectural design choices. The successfully trained network is finally applied as a CNN-retracker to the full time series of CryoSat-2 low resolution mode (LRM) waveforms over the Antarctic ice sheet. We will show the CNN retrieved SEC and compare it to estimates of conventional retrackers like OCOG or ICE2. Our preliminary results show reduced uncertainty and a strongly reduced time variable radar penetration, making backscatter or leading edge corrections typically applied in SEC processing obsolete. This technique provides new opportunities to utilize convolutional neural networks in altimetry, waveform retracking, and processing altimetry data, which can be applied to historical, recent, and future altimetry missions.
Modelling Soil Temperature and Soil Moisture in Space, Depth, and Time with Machine Learning Techniques
Authors: Baumberger Maiken , Adorf Linda , Haas Bettina , Meyer Nele , Meyer Hanna
Published: February 2023

Soil temperature and soil moisture variations have large effects on ecological processes in the soil. To investigate and understand these processes, high-resolution data of soil temperature and soil moisture are required. Here, we present an approach to generate data of soil temperature and soil moisture continuously in space, depth, and time for a 400 km² study area in the Fichtel Mountains (Germany). As reference data, measurements with 1 m long soil probes were taken. To cover many different locations, the available 15 soil probes were shifted regularly in the course of one year. With this approach, around 250 different locations in forest sites, on meadows and on agricultural fields were captured under a variety of meteorological conditions. These measurements are combined with readily available meteorological data, satellite data and soil maps in a machine learning approach to learn the complex relations between these variables. We aim for a model which can predict the soil temperature and soil moisture continuously for our study area in the Fichtel Mountains, with a spatial resolution of 10 m x 10 m, down to 1 m depth with segments of 10 cm each and in an hourly resolution in time. Here, we present the results of our pilot study where we focus on the temperature and moisture change within the depth down to 1 m at one single location. To take temporal lags into account, we construct a Long Short-Term Memory network based on meteorological data as predictors to make temperature and moisture predictions in time and depth. The results indicate a high ability of the model to reproduce the time series of the single location and highlight the potential of the approach for the space-time-depth mapping of soil temperature and soil moisture.
8-year hydroclimate reconstruction from Christmas Island (Indian Ocean); an extended record of variability in the Indonesian Throughflow
Authors: Hargreaves Jessica A. , Abram Nerilie , Mallela Jennie
Published: February 2023

Future climate trends indicate that changes in temperature and precipitation are likely to influence global supply chains, agricultural productivity, water security, health and well-being; particularly in densely populated nations across the southeast Indian Ocean region. The Indonesian Throughflow is an ocean current that transports low-latitude, warm and relatively fresh water from the western Pacific into the eastern Indian Ocean. It is thought that variability and changes in the Indonesian Throughflow have significant impacts on the climate and oceanography of the Indo-Pacific region. The short coverage of observational records makes assessments of hydrological changes across the region challenging on longer timescales, with changes before the 1970s being particularly unreliable. An extended record of Indonesian Throughflow variability needs to be established to contextualise changes and improve model projections of future variability.Christmas Island, located in the southeast Indian Ocean (not to be confused with the Pacific Ocean Kiritimati Island), is located along an outflow of the Indonesian Throughflow. This Island is an ideal location to develop new palaeo-reconstructions of sea surface temperature and hydroclimate, extending our understanding of Indonesian Throughflow variability. Here we present a newly developed coral palaeoclimate reconstruction for Christmas Island, covering the last 118 years at approximately monthly-fortnightly resolution. Corals are sensitive recorders of critical environmental variables, including sea surface temperature and hydroclimate through the analysis of paired stable oxygen isotopes (δ18O) and trace element (Sr/Ca) ratios. This reconstruction consists of a composite of four newly developed coral records and one previously published record and provides a newly developed δ18Osw variability record for the region. The newly developed δ18Osw coral reconstruction correlates strongly with salinity variability, however, presents a weak relationship to in-situ precipitation, indicating that coral hydroclimate reconstructions from Christmas Island likely isolate salinity variability associated with changes in the strength of the Indonesian Throughflow. This relationship highlights the importance that ocean advection plays on δ18Osw variability at this site. Comparisons to both observational records of the Indonesian throughflow, and previously published coral δ18Osw records from the Ombai Strait (Timor), a major outflow passage, reveal strong relationships to variability at Christmas Island. The Christmas Island reconstruction provides a unique opportunity to extend current knowledge of the Indonesian Throughflow beyond the observational record. This Christmas Island record also provides an opportunity to evaluate the impact that interannual to multidecadal variability has on the climate across the southeast tropical Indian Ocean.
Study on Forest Management System Considering Ecosystem Services in Islands in South Korea
Authors: Park Chanwoo , Lee Bora , Jo Jang-Hwan , Lee Kwang Soo
Published: February 2023

Forest ecosystem in islands should be managed based on site-specific management plan since it has different biological and cultural characteristics compared to inland forest ecosystem related to isolation, uniqueness, and vulnerability. There are more than 3,800 islands in South Korea and half of them have forest ecosystem. The area of forest ecosystem in island is 2,267 km2, 3.5% of total forest area in South Korea. The need for island forest management is widely recognized, but it is not cost- and time-effective to manage island forest ecosystem intensively same as inland forest ecosystem. we are conducting research to develop suitable management system for island forest ecosystem through the following process. (1) constructing island database, (2) identifying effective factors to categorize island forest type, (3) determining and quantifying key ecosystem services and funtions, (4) developing technical and silvicutural method to enhance ecosystem functions, (5) seeking to legislate for continuity and legitimacy to island forest ecosystem maintenance. Database on the island forest ecosystem with 49 fields containing socio-economical, biological and ecological information was constructed. Then islands were categorized into 6 types using 16 effective factors to categorize derived from expert delphi survey. Also, 10 key ecosystem services importantly in domestic island forests were determined as follows: natural resources, medicinal plants, watershed and freshwater, erosion control, biological control, aesthetic appreciation and inspiration, forest recreation, cultural heritage, species habitants, and maintenance of genetic diversity. The key ecosystem services in each type of island forest are expected to provide priorities and directions for forest management.
Incorporating socio-hydrological contexts for systemic drought vulnerability in forested cold climates - a conceptual framework.
Authors: Stenfors Elin , Blicharska Malgorzata , Grabs Thomas , Teutschbein Claudia
Published: February 2023

In a changing climate, drought risk and vulnerability assessments are becoming increasingly important. Following the global call for proactive drought risk management, drought vulnerability assessments are progressively taking its stage in the drought research community. As the manifestation of drought vulnerability is dependent on the social, ecological and hydroclimatic context in which it occurs, identifying vulnerability factors relevant for specific climatological and ecological regions may improve the quality of vulnerability assessments. Meanwhile, a holistic overview of factors affecting vulnerability in polar and cold climates is currently lacking, although recent events (such as the 2018 European drought affecting large parts of Scandinavia) have reminded us that droughts are a prevalent risk even in colder climate zones. These regions are home to large socio-hydrological systems including urban areas, energy systems, agricultural practices, and the boreal forest. By conducting an interdisciplinary systematic literature review, the manifestation and conceptualization of drought vulnerability was identified for forested ecoregions in the Köppen-Geiger D and E climates. Vulnerability factors, as described by several scientific disciplines, were identified and combined into a conceptual framework for drought vulnerability in the study region. The results demonstrate the wide range of conceptualizations that exist for assessing drought vulnerability, the key sectors studied in the region, as well as the thematic differences between sectors such as forestry, water supply and agriculture. The developed conceptual framework adopts a novel approach, categorising vulnerability factors by their location in a socio-hydrological system, and their relation to blue or green water sources. This allows for identification of systemic vulnerability patterns for different drought types as well as vulnerability factors that may be universal for these socio-hydrological systems independent of the drought type they are exposed to. The results provide new insights into regional differences in drought vulnerability and a base for stakeholders performing proactive drought risk assessments in the study region.
Phase diagram of pure iron in Earth's core from deep learning
Authors: Li Zhi , Scandolo Sandro
Published: February 2023

Iron is considered to be the main component of the Earth's core. Substantial efforts have been made to understand its phase diagram and physical properties at extreme conditions. However, it remains debated about how the atoms in solid iron are arranged at Earth's core conditions, where possible candidates include hexagonal close-packed (hcp), body-centred cubic (bcc), and face-centred cubic (fcc) structures. As crystal structure and physical properties are closely related, there is also a significant uncertainty in the properties of Earth's core, such as elasticity, heat conductivity, and density, making the accurate interpretation of seismic observations difficult. Here we aim to study the phase stability of solid iron at Earth's core conditions. For this, a deep-learning interatomic potential was developed with ab initio accuracy but is more cost-effective. To further check the performance of such potential, we examine the elastic and plastic behaviour of hcp iron and the effects of structural defects at inner core conditions [1]. We then compute the Gibbs free energy of the bcc, fcc, hcp and liquid phases by performing large-scale molecular dynamics simulations. The calculated free energy allows for determining the phase stability of solid iron in Earth's core.[1] Li, Z., & Scandolo, S. (2022). Elasticity and viscosity of hcp iron at Earth's inner core conditions from machine learning-based large-scale atomistic simulations. Geophysical Research Letters, 49, e2022GL101161.
Using automated web data mining for natural hazard assessment
Authors: Rosi Ascanio , Franceschini Rachele , Casagli Nicola , Catani Filippo
Published: February 2023

Landslides and floods in Italy are the most frequent and diffuse natural hazards causing fatalities and damages to urban areas. Traditional methods as photo-interpretation, remote sensing or retrieval data from technical reports are the most common to set up event inventories. These systems rarely rely on automated or real-time updates. The retrieval of data, using specific data mining algorithms, from newspapers allows continuous feedback from real world and can further extend the exploitable data. Exploiting the data from mass media allows to get information about disaster situations with a relatively high temporal and spatial resolution to map natural hazards across various locations. Several techniques have been developed to mine data for different natural hazard, but rarely applied about landslide and flood news. The algorithm Semantic Engine to Classification and Geotagging News (SECaGN), based on a semantic engine, automatically retrieves information from online newspaper. 184.322 newspaper articles have been harvested from 2010 to 2019, referred to 32.525 landslide news and to 34.560 floods news in Italy. In this work, the data harvested by SECaGN underwent to a manual classification based on news relevance, localization accuracy and time of publication. Most of the news referred to recent events or are generically referred to landslide or floods (remediation work, hazard scenarios) and only a minimum part it was made up by wrong news. This classification allowed to identify the “true news” and to reject the data not appropriate, reducing the uncertainties.
The harvested data have been used to identify the media impact of the events (both landslides or floods), their temporal distribution and those areas where more events happened, allowing a fast hazard estimation of the Country.
The retrieved news data have been then compared with traditional sensors (e.g. rain gauges) and official reports about victims, damages, funds for soil protection and risk maps. Results did not show any clear correlation between the distribution of news and the other parameters, but it resulted that the regions that experienced a relevant number of events recorded lower funds for soil protection and vice versa.
In conclusion, this work allowed to demonstrate the possibility of using automatically retrieved data from newspaper to create a reliable landslide (and flood) inventory, to be used as a proxy for hazard assessment over wide areas and to investigate the distribution of the phenomena and their correlation with other parameters, providing a powerful tool for a rapid hazard assessment in support of public authorities and decision makers.
Denoising InSight’s marsquake recordings with deep learning
Authors: Dahmen Nikolaj , Clinton John , Meier Men-Andrin , Stähler Simon , Ceylan Savas , Charalambous Constantinos , Kim Doyeon , Stott Alexander , Giardini Domenico
Published: February 2023

Marsquake recordings by NASA’s InSight seismometer often have low signal-to-noise ratios (SNR) owing to low marsquake amplitudes - only a handful of events are over M3.5 and epicentral distances are large, due to the single station being located in a seismically quiet region, and highly fluctuating atmospheric, spacecraft and instrumental noise signals.We have previously shown [1] how deep convolutional neural networks (CNN) can be used for 1) event detection - thereby producing an event catalogue consistent with the manually curated catalogue by the Marsquake Service (MQS) [2], and further extending it from 1297 to 2079 seismic events - as well as for 2) separating event and noise signals in time-frequency domain. Due to the low number of events readily-available for network training, we trained the CNN on synthetic event data combined with recorded InSight noise.Here, we construct a semi-synthetic data set (with real marsquake & noise data) to assess the denoising performance of the CNN w.r.t. to various evaluation metrics such as SNR, signal-distortion-ratio, cross-correlation, and peak amplitude of the recovered event waveforms, and compare modifications of the CNN architecture and the training data set.For a large number of identified events [1,2] no distance estimates are available (or only with high uncertainty), and for all but a small subset the back azimuth is unclear, as the relatively high background noise often obscures this information in the waveforms. We explore how the denoised waveforms can support the phase picking and polarisation analysis of marsquakes, and with that their localisation, as well as their general characterisation. References:[1] Dahmen et al. (2022), doi: 10.1029/2022JE007503[2] Ceylan et al. (2022), doi: 10.1016/j.pepi.2022.106943
Chemical and microbial mass balances in microbial turnover of two easily degradable carbon substrates
Authors: Yousaf Ubaida , Vonhoegen Denise , Thiele-Bruhn Sören
Published: February 2023

Recent research indicates that soil microbes play a significant role in the formation and turnover of soil organic matter (SOM). Thus, OM is metabolized by microorganisms through intracellular and extracellular enzymatic activity, with one portion of it being converted into biomass and another being respired for energy. This causes an energy and matter flux that is adjusted and slowed down by ongoing recycling of the matter and residual energy. Matter and energy are conserved as much as possible throughout repeating microbial growth cycles, resulting in an "energy use channel," and/or storage as necromass. Soil fertility and several other soil functions depend on the activity of diverse soil microbial populations and, consequently, on continual energy and carbon flows within the soil system. Fluxes and stoichiometry concerns must be considered for the maintenance of microbial diversity and ecosystem activities in soil, including C storage. To comprehend C turnover and sequestration in terrestrial ecosystems, further knowledge of the relationship between element cycling and energy fluxes is required. In this project, we present a conceptual overview of microorganisms as mediators of SOM production, we do that by investigating seven carbon substrates with varying complexity with the same model soil (fertilized Dikopshof) in five different incubation experiments.In the first experiment, we study the effect of substrate size (Glucose — 180 Da, α — 1,4-maltotetraose — 666,6 Da). We hypothesize that exoenzymes would be required to degrade any substrate greater in size than 600 Da, meaning different CUE/EUE due to a change in the process type from growth-oriented processes — high energy flux for glucose degradation to the adaption-oriented processes for the larger substrate, i.e., maltotetraose in this case. The substrates were labelled with 13C to determine various carbon pools in the samples. Destructive sampling was used to obtain subsamples from 6 different time points. Aminosugars and acids were used as markers of microbial biomass/necromass. Chloroform fumigation extraction was performed to determine microbial biomass of carbon and nitrogen. In combination with further data to calculate the microbial quotient (Cmic/OC), the respiratory quotient (qCO2= resp./Cmic), and CUE. Gas flux sampling and isotope selective CO2 analysis to determine the differences in the turnover of the substrates (Energy consumption respiration) The energy accumulation includes the formation of additional biomass, necromass, and metabolites. Analysis of C, H, N, S, O, and P to calculate the stoichiometry of OM.  
Plate interface frozen at the very end of continental subduction
Authors: Mendes Kevin , Agard Philippe , Plunder Alexis , Herviou Clement
Published: February 2023

Continental subduction and collision are not merely follow-ups of oceanic subduction but mark the transition from lithospheric-scale deformation localized along the subduction interface to crustal-scale deformation distributed across the orogen. In order to unravel the processes typifying the dynamic changes from oceanic subduction to collision, we have characterized pressure-temperature (P-T) and spatio-temporal evolution of rocks on either side of the tectonic contact (Briançonnais/Liguro-Piemont contact – Br/LP contact) separating the subducted oceanic remnants from the subducted continental fragments along the length of the Western Alps. West of the contact, the Briançonnais zone is considered as a micro-continent composed of pre-Alpine basement and Paleozoic to Meso-Cenozoic cover units. East of the contact, the Liguro-Piemont zone corresponds to a nappe-stack, with three groups of oceanic (upper, middle and lower) units. The Piemont zone is pinched in between the two in the southern part of the Western Alps and correspond to the distal part of the Briançonnais continental margin.Results indicate that the maximum temperature and pressure difference on each side of the contact is generally < 30°C and < 0.3 GPa, evidencing that (i) no significant metamorphic gap exists between both sides and that (ii) offscraping of the continental fragments occurred at the same depth as the oceanic ones. The dataset also shows a northward increase of peak P-T conditions from ~300°C-1.2 GPa to ~500°C-2.0 GPa. The preservation of similar P-T conditions on both sides of the Br/LP contact can tentatively be assigned to either (1) offscraping of the Liguro-Piemont and later of the Briançonnais at similar depths or (2) entrainment and joint burial of the Liguro-Piemont (previously accreted or subducted) fragments together with the Briançonnais margin. The latter hypothesis, however, is not supported by the ~10 My gap between the peak burial of the Briançonnais and Liguro-Piemont zones. The recurrent depth range of the various units, which reflects systematic variations of slicing and mechanical coupling along the plate interface (Herviou et al., 2022), suggests that (1) similar slicing mechanisms and strain localization prevailed during both oceanic and continental subduction and (2) shows that the Br/LP contact represents a frozen-in subduction interface. The end of high-pressure and low-temperature metamorphism and continental subduction at ~33 Ma would thus mark the stalling of subduction interface dynamics and the onset of strain distribution across the plate interface and into the lower plate. Herviou, C., Agard, P., Plunder, A., Mendes, K., Verlaguet, A., Deldicque, D., Cubas, N., 2022. Subducted fragments of the Liguro-Piemont ocean, Western Alps: Spatial correlations and offscraping mechanisms during subduction. Tectonophysics 827, 229267. https://doi.org/10.1016/j.tecto.2022.229267
Sinkhole risk assessment by using machine learning model: the case study of Guidonia-Bagni di Tivoli plain (Rome), Italy
Authors: Bianchini Silvia , Confuorto Pierluigi , Intrieri Emanuele , Sbarra Paolo , Di Martire Diego , Calcaterra Domenico , Fanti Riccardo
Published: February 2023

Sinkholes that occur in settled carbonate lands can be a critical source of risk for human properties and activities since they can abruptly produce serious damage to property and people in densely populated flat areas. This work presents a sinkhole susceptibility and risk assessment mapping in Guidonia-Bagni di Tivoli plain (Central Italy), which is a carbonate sinkhole-prone study area where sudden occurrences of sinkholes have happened in past and recent times. We consider a point-like sinkhole inventory and a series of environmental sinkhole-controlling factors on the study area, related to its geo-litho-hydrological asset, i.e. travertine thickness, and to its terrain deformational scenario, i.e. ground motion rates derived from InSAR COSMO-SkyMed imagery. A sinkhole susceptibility map was generated by means of maximum entropy algorithm  - MaxEnt model – and it was then combined with data on vulnerability and elements-at-risk economic exposure derived from cadastral inventories and market and income values, in order to provide a final sinkhole risk map of the Guidonia-Bagni di Tivoli area. The results show that areas at higher risk covers about 2% of the total study area and primarily relies on the zoning of the main urban fabric. In particular, it is worth to highlight that 5% of the whole road-network pavement and 27% of all the residential buildings fall into higher risk classes. Outcomes of this work reveal the potential of MaxEnt model to assess sinkhole susceptibility for predicting sinkhole areas, either provide a sinkhole risk map as a useful tool for geohazard risk and urban planning management strategies.
Effects of fire intensity on CO2 exchange in an arctic tundra ecosystem
Authors: Xu Wenyi , Elberling Bo , Ambus Per Lennart
Published: February 2023

Recently, the frequency and intensity of wildfires has been increasing in the Arctic as a result of climate change. However, there is still little knowledge on the effects of fire intensity on carbon dioxide (CO2) exchange in arctic tundra ecosystems. We conducted an experimental fire of different burn intensity (i.e., low intensity, high intensity and unburned control) to investigate effects of fire intensity on soil biogeochemical cycles and surface CO2 fluxes over four growing seasons in an arctic heath tundra, West Greenland. Post-fire soil temperatures and soil moisture increased with increasing fire intensity by up to 2.2 ℃ and 18 vol%, respectively. The high-intensity fire also significantly increased soil nitrate concentrations 1 day post fire, but this effect disappeared 1 year post fire. There were no significant effect of fire intensity on soil carbon and phosphorus availability or microbial biomass. The ecosystem shifted from a net CO2 sink to a net CO2 source immediately after the fire, because of the reductions in photosynthetic activity. 1 year post fire the low-intensity burned plots have turned into a net CO2 sink, while the high-intensity burned plots were a net CO2 source for the entire study period. This suggests that the time needed for the burned ecosystem to turn into a net CO2 sink increases with increasing fire intensity. Fire intensity had no effect on ecosystem respiration (ER) immediately after the fire, likely because the increases in microbial respiration caused by elevated soil temperatures and moisture and soil nitrogen availability have offset the decreases in plant respiration. However, 1-3 years after the fire, the high-intensity fire significantly reduced ER rates, suggesting that the moderate increases in microbial respiration only caused by elevated soil temperatures and moisture could not balance out the decreases in plant respiration. Overall, compared with low-intensity fire, high-intensity fire not only combusts more biomass or soil organic matter and releases more CO2 during the fire, but also prolongs the duration of the burned areas as a net CO2 source and consequently enhances post-fire CO2 losses.
Steady and transient solar wind sources, acceleration profiles and rotation across the solar activity cycle
Authors: Pinto Rui , Rouillard Alexis , Finley Adam
Published: February 2023

The solar wind is an uninterrupted flow of highly ionised plasma that streams from compact sources at or near the Sun, accelerates across the low solar corona, and expands into the whole interplanetary space. The physical properties of any wind streams thus reflect the characteristics of their source regions and those of the extended zones of the corona they cross, and are affected by the time-varying strength and geometry of the global background magnetic field.  The rotational state of the solar corona also plays a fundamental role in a wide range of solar wind phenomena, but is much less well-known than that of the photosphere. In addition, surface dynamics and magnetic field evolution drive perturbations to the corona and wind that can either be transient or long-lasting.We investigate the geometry and spatial distribution of solar wind sources by means of an extended time series of data-driven 3D simulations that cover nearly 2 activity cycles. We furthermore examine the corresponding solar wind acceleration profiles (radial trends) as a function of source latitude and time, and highlight consequences for the interpretation of Parker Solar Probe (PSP) and Solar Orbiter (SolO) in-situ measurements (especially as the latter moves away from the ecliptic plane). We also highlight impacts on the rotation profile of the solar corona and on the occurrence of regions of enhanced poloidal and toroidal flow shear that can drive plasma instabilities. Finally, we point out directions to assess the effects of surface transient phenomena driven by flux emergence on the properties of the solar wind. 
Thermal adaptation evolution and dispersal of the terrestrial biosphere regulates Earth’s long-term climate
Authors: Rogger Julian , Mills Benjamin , Gerya Taras , Pellissier Loïc
Published: February 2023

Sustained habitable conditions and the evolution of complex life on Earth depend on efficient climate regulation mechanisms that keep carbon fluxes between geologic reservoirs and the atmosphere-ocean system in balance. The terrestrial biosphere plays an important role in regulating the long-term climate by controlling burial rates of photosynthetically fixed CO2 as well as by mediating CO2 consumption through silicate mineral weathering during plant nutrient acquisition. These long-term carbon sinks balance out carbon inputs to the atmosphere-ocean system by processes including volcanism or the oxidative weathering of buried organic carbon. Current biogeochemical models of the Phanerozoic Earth neglect that the strength of the impact of the terrestrial biosphere on global carbon fluxes is subject to evolutionary dynamics and that it depends on how well the biosphere is adapted to prevailing environmental conditions [1]. Here, we develop a theoretical model to reconstruct global organic and inorganic carbon fluxes over the last 390 Myrs. The model includes eco-evolutionary processes underlying the thermal adaptation, such as the dispersal of terrestrial biomes in response to climatic changes and the in situ adaptive evolution towards the local environment. We show that the speed of evolutionary adaptation of the terrestrial biosphere to climatic shifts strongly affects the long-term atmosphere-ocean carbon mass balance. When considering a slow rate of thermal adaptation of the biosphere, resulting in reduced organic carbon burial and silicate weathering rates following temperature shifts, a closer balance of reconstructed Phanerozoic carbon inputs and outputs to and from the atmosphere-ocean system is obtained. Such a balance is a prerequisite to maintain habitable conditions on Earth’s surface on a multi-million-year timescale. We argue that the climate evolution of the Phanerozoic Earth is strongly defined by biological and evolutionary processes. Understanding these biological dynamics and how they shape the interactions between Earth’s biosphere, geosphere and the climate system may help to understand large shifts in Phanerozoic temperatures and the development of the atmospheric composition of the planet.[1] Mills, B.J. et al. Modelling the long-term carbon cycle, atmospheric CO2, and Earth surface temperature from the late Neoproterozoic to present day. Gondwana Research 67, 172-186. DOI: 10.1016/j.gr.2018.12.
Machine learning assisted fast forward 3D modelling for time-domain electromagnetic induction data – lessons from a simplified case
Authors: Deleersnyder Wouter , Dudal David , Hermans Thomas
Published: February 2023

In (time-domain) Electromagnetic Induction (EMI) surveys, an image of the electrical conductivity of the subsurface is obtained non-invasively. An accurate interpretation of the data is computationally expensive as it requires a full (high fidelity) 3D simulation of the induced electric currents embedded within an iterative and ill-posed inverse problem. Therefore, this forward model is usually approximated with a 1D forward model (low fidelity model) which only considers horizontal layers and for which fast analytical forward models exist. Recent work [1] has shown that a multidimensional forward model can be relevant in time-domain Airborne EM inversion. To be more precise, we provided an appraisal tool for quasi/pseudo-2D inversion to indicate that fast forward 3D modelling for time-domain (Airborne) EM data is still worthwhile and, in fact, necessary, in some areas. Surrogate modelling and machine learning may replace 3D forward modelling on a mesh during a 3D inversion.In this contribution, we first demonstrate the initial steps towards creating an efficient surrogate model for 3D modelling with only 5000 samples in the training dataset. Rather than predicting the high-fidelity or 3D data directly, we predict the relative error between the high and low fidelity data. The idea behind this approach is that predicting the difference with a relatively good low-fidelity model is easier and more robust than trying to find a surrogate for the full data set. The computation of low fidelity data via the 1D approximation is no longer a computational burden, yet it explains most of the variability in the observed data. The residual variability, originating from the non-1D nature of the subsurface, is predicted with a Gaussian process regression model. Combining the low-fidelity model with a trained correction term via Machine Learning saves significant computation times. We show encouraging results, currently limited to two layers, where the trained surrogate model proves to produce a significant ‘learning gain’ in 92,5% of the cases (see Figure 1), meaning that it can significantly reduce the residual multidimensional variability. The cases where the surrogate model makes the prediction of the high-fidelity data worse, occur at the limits of the training data space, indicating that those cases could be resolved by generating more training data in those areas.Figure 1 – The learning gain on the test dataset by using the trained surrogate model References[1] Deleersnyder, W., Dudal, D., & Hermans, T. (2022). Novel Airborne EM Image Appraisal Tool for Imperfect Forward Modeling. Remote Sensing, 14(22), 5757. https://doi.org/10.3390/rs14225757
Development of a Web Based Decision Support System to Provide Relevant Climate Indicators for Climate Change Adaption
Authors: König Lorenz , Teucher Mike , Ziegler Katrin , Abel Daniel , Weber Torsten , Paeth Heiko , Conrad Christopher
Published: February 2023

The African continent faces various challenges and numerous risks due to current and future climate change. To strengthen the resilience of West African societies in the sectors of agriculture, food security, water and risk management, adaption measures need to be implemented in time. In the WASCAL-LANDSURF project, an earth system model for West Africa is developed to enable high-resolution regional climate change information. The obtained data must be accessible to the public so that interested stakeholders and smallholders can incorporate them into their decision-making processes. To realize this, a web based spatial decision support system (SDSS) is developed with state-of-the-art open-source technologies to give information on climate change as well as relevant cross-sector indicators. For successful co-development, stakeholder workshops were held to identify important key functionalities and indicators that need to be implemented. The SDSS will be multilingual and easy to use to ensure an extensive range of applications. Users will have the possibility to familiarize themselves with the Web Portal by means of different guides in order to ease the entry into the SDSS. The current prototype supports map and diagram visualization and selection of various indicators and climate data, as well as query functionalities for different West African regions. Many other functionalities, such as the possibility to download data and statistical outputs of selected indicators, will be integrated soon. The final web portal will give users the opportunity to include climate indices in a simple and clear way into their decision-making process to strengthen their resilience towards climate change.
Microfossil-based reconstruction of latitudinal thermal gradients in the Southern Ocean during MIS11c
Authors: Hernández-Almeida Iván , Hirt Janik , Renaudie Johan
Published: February 2023

The Southern Ocean (SO) is a region particularly sensitive to the anthropogenic global warming because of the raising ocean temperatures, leading to latitudinal shifts of oceanographic fronts which govern the position of the South Westerly Winds (SWW) in the SO. Sediments represent a natural climate archive that allows to observe changes in Earth’s systems only affected by natural forcing. In this sense, Marine Isotope Stage (MIS) 11c (∼426–396 ka) is the most similar climate state to the ongoing climate warming that we are facing today, but quantiative climate reconstructions in the SO for this period are scarce. Radiolarians (zooplankton) live in a wide range of depths in the water column and are very abundant in sediments throughout the Neogene in the SO.  Recent radiolarian databases and transfer functions for the SO (Lawler et al. 2021; Civel-Mazens et al. 2022) enable reconstructing quantitatively past climate. For this, three sediment cores, drilled during IODP Expedition 382 and located along latitudinal gradient in the Atlantic sector of the SO (between 53.2°S and 59.4°S), were studied for their fossil radiolarian assemblage composition for the interval corresponding to MIS 11c. Application of the newly developed radiolarian transfer functions to the fossil radiolarian assemblages in these three cores enabled the reconstruction of ocean temperatures and thermal gradients in the SO during MIS 11c. These reconstructions will be used also to infer the position of the oceanographic frontal zones and the position of the SWW in this sector of the SO in the past, which are important for promoting upwelling nutrient rich bottom waters and degassing of deeply sequestered CO2 during the interglacial maxima.References:Civel-Mazens, M., Cortese, G., Crosta, X., Lawler, K. A., Lowe, V., Ikehara, M., & Itaki, T. (2022). New Southern Ocean transfer function for subsurface temperature prediction using radiolarian assemblages. Marine Micropaleontology, 102198.Lawler, K. A., Cortese, G., Civel-Mazens, M., Bostock, H., Crosta, X., Leventer, A., & Armand, L. K. (2021). The Southern Ocean Radiolarian (SO-RAD) dataset: a new compilation of modern radiolarian census data. Earth System Science Data, 13(11), 5441-5453.
The two towers: CO2 fluxes after wildfire in managed Swedish boreal forest stands
Authors: Kelly Julia , H. Doerr Stefan , D'Onofrio Claudio , Holst Thomas , Lehner Irene , Lindroth Anders , Santín Cristina , Soares Margarida , Kljun Natascha
Published: February 2023

Wildfires have been effectively suppressed in the managed boreal forests of Fennoscandia for over a century. However, recent extremely hot and dry summers have highlighted the vulnerability of these forests to increasing wildfire frequency as a result of climate change. The carbon stored by managed forests plays a key role in many national climate mitigation strategies and more data is needed to assess how forest management shapes the carbon balance of these forests, including those recovering from wildfire. We established two eddy covariance towers in the area burnt by the Ljusdal fire, which affected over 9000 ha during the extreme 2018 wildfire season in Sweden. The two towers measured CO2 fluxes during the first four growing seasons after the fire at two Pinus sylvestris stands with contrasting fire impacts and forest management schemes. At one site, a mature stand had survived low severity fire but was then salvage-logged and reseeded (6 months after the fire), whilst the other site represented a young stand that was killed by high severity fire and replanted with Pinus sylvestris seedlings (2 years after the fire). After the fire, both sites were net CO2 sources at the annual scale. However, the site with dead young trees and replanted seedlings showed a faster recovery towards becoming a CO2 sink, with days of net CO2 uptake during the peak of the growing season three years after the fire. Preliminary results suggest that similar magnitudes of carbon were emitted as CO2 in the first 4 years after the fire compared to the carbon emitted during the fire itself, underlining the importance of monitoring forest CO2 fluxes and the impacts of management decisions during the initial post-fire years.
DTRF2020: Strategy, Results and Data Set
Authors: Seitz Manuela , Blossfeld Mathis , Glomsda Matthias , Angermann Detlef , Rudenko Sergei , Zeitlhoefler Julian , Seitz Florian
Published: February 2023

The DTRF2020 is the ITRS 2020 realization of the ITRS CC at DGFI-TUM.It was published earlier this year. The calculation of the DTRF2020 is based on a two-step approach: First, after a data pre-analysis, one TRF (normal equation system, NEQ) per technique, i.e. VLBI, SLR, GNSS and DORIS, is calculated. Second, the TRF NEQs are combined to the DTRF2020 solution. For the first time, all previously modeled non-tidal loading (NTL) components provided by IERS GGFC, i.e., the atmospheric, hydrological, and oceanic parts, are considered to reduce NTL signals in the station motions from the input NEQ. Post-seismic deformation (PSD) signals in station position time series are approximated by combinations of logarithmic and exponential functions and reduced from the NEQ in the same way as the NTL signals in the first step of our approach.  The published dataset includes the DTRF2020 solution itself, i.e., station positions at the reference epoch 2010.0 and station velocities, as well as the consistently estimated EOP series. In addition, the model-based NTL time series used in the DTRF2020 calculation, the parameters of the PSD approximation functions, and the corresponding time series of the approximation signal considered in DTRF2020 (over the DTRF2020 time period) are provided for the users. Furthermore, residual time series of station positions and SLR translation time series are made available (the latter indicate the deviation of the instantaneous from the mean center of mass realized in DTRF2020). In this presentation, we discuss the final results of DTRF2020 and give an overview of the DTRF2020 dataset and the features it provides for its application. We also discuss the consistency of DTRF2020 with respect to its predecessor DTRF2014 and the limits of accuracy of reference frames. Further, we address the need for consistency with respect to datasets that will be used in DTRF2020 applications, such as the SLR Data Handling File and GNSS satellite PCV.