bluuebunny/crossref_metadata_2025 huggingface dataset:
Gaming towards a sustainable future
Authors: Rebel Karin , Kasch Julia , Dotinga Rose-Anne , Gilissen Herman Kasper , Groenewolt Simon , Hessels Peter , Smeenk Tara , Swart Julia , Werning Stefan , Vervoort Joost
Published: March 2022

A sustainable future is both dependent on and of the utmost importance for all people living on the planet. In 2015, the United Nations member states adopted the ‘Sustainable Development Goals’ (SDGs) as ‘a blueprint to achieve a better and more sustainable future for all’. Higher Education plays a critical role in achieving the SDG agenda as it engages students with the 17 goals both cognitively and affectively through research, teaching, collaboration with stakeholders, and campus practices. Achieving the ambitions described above requires innovative thinking and going beyond standard ways of curriculum development to account for how the goals affect students not just professionally but also personally. Hence, over the past 3 years, we developed a unique location-based, multiplayer mobile serious game: Utrecht2040. This game aims to teach students from all backgrounds and disciplines about key sustainability challenges in a systemic, interdisciplinary, and applied manner. Moreover, it aims to give them the tools to bring in their own personal and disciplinary perspective, and become agents of change. Utrecht2040 focuses on sustainability in urban contexts and can be played in any city.

So far, over 2000 students played the Utrecht2040 game, across different faculties and bachelor programs. Our research team has conducted pre- and post-questionnaires with a total number of 225 participants. The players reported a positive effect on their knowledge and appreciation of the SDGs as a proxy for sustainability, specifically in urban contexts, which was significant for all groups. Students also indicated that playing in a group and interacting with citizens gave them new insights about the future possibilities for the city as well as the motivation to act on these insights. Over the next years, we will extend the game to all bachelor students at Utrecht University, as well as to other universities at the (inter-)national level, e.g. within the European University CHARM-EU, an alliance of the European Universities Initiative, where they will play it in their master’s program ‘Global Challenges in Sustainability’.
Evaluation of a coupled climate ice sheet model over the Greenland ice sheet and sensitivity to atmospheric, snow and ice sheet parameters
Authors: Lang Charlotte , Lee Victoria , Sherriff-Tadano Sam , Gandy Niall , Gregory Jonathan , Ivanovic Ruza , Gregoire Lauren , Smith Robin S.
Published: March 2022

As part of a project working to improve coupled climate-ice sheet modelling of the response of ice sheets to changes in climate across different periods since the Last Glacial Maximum, we present simulations of the modern Greenland climate and ice sheet using the FAMOUS-BISICLES model.

FAMOUS-BISICLES, a variant of FAMOUS-ice (Smith et al., 2021a), is a low resolution (7.5°X5°) global climate model that is two-way coupled to a higher resolution (minimum grid spacing of 1.2 km) adaptive mesh ice sheet model, BISICLES. It uses a system of elevation classes to downscale the lower resolution atmospheric variables onto the ice sheet grid and calculates surface mass balance using a multilayer snow model. FAMOUS-ice is computationally affordable enough to simulate the millennial evolution of the coupled climate-ice sheet system, and has been shown to simulate Greenland well in previous work using the Glimmer shallow ice model (Gregory et al., 2020).

The ice sheet volume and area are sensitive to a number of parametrisations related to atmospheric and snow surface processes and ice sheet dynamics. Based on that, we designed a perturbed parameters ensemble using a Latin Hypercube sampling technique and ran simulations with climate forcings appropriate for the late 20th century. The ice sheet area and volume are most correlated to parameters that set the snow/firn albedo while the relationship is less simple for parameters related to clouds and precipitation.

We compare FAMOUS-ice SMB and coupled behaviour against the more sophisticated, higher resolution, CMIP6-class UKESM-ice coupled climate ice sheet model for a late 20th century simulation as well as an abrupt 4XCO2 experiment.

Our simulations produce a large range of climate and ice sheet behaviours, including a stable control state for the modern Greenland, and we have been able to highlight the sensitivity of the system to other sets of parameters and future changes in climate.
Turbulence modified by velocity shear in coronal mass ejection sheaths
Authors: Soljento Juska , Good Simon , Osmane Adnane , Kilpua Emilia
Published: March 2022

Fast coronal mass ejections (CMEs) drive shock waves ahead of them. The turbulent sheath region between the shock and the CME itself contains magnetic field and velocity fluctuations on a broad spectrum of frequencies. In this work we aim to characterise the direction and source of solar wind fluctuations at MHD fluid scales in CME-driven sheaths near Earth. One possible source for these fluctuations is velocity shear, which are common occurrences in CME-driven sheaths. Here we first identify velocity shear as it occurs and then relate that to signatures of new fluctuations being created locally in the sheath. Turbulence parameters such as cross helicity, residual energy, Elsasser ratio, and Alfvén ratio are calculated, and they are correlated against large-scale signatures of velocity shear. Findings indicate a clear association between velocity shear and locally generated fluctuations, as well as a balance in the directionality of these new fluctuations, i.e., they tend to propagate equally towards and away from the Sun. In contrast, most solar wind is typically dominated by anti-sunward fluctuations.
How gravity, wind, rain and surface runoff drive plastic transport on land
Authors: Mellink Yvette , van Emmerik Tim , Mani Thomas
Published: March 2022

To accurately predict the transport routes of mismanaged plastic waste from land-based sources to their sinks, terrestrial plastic transport models require a robust empirical basis. The main driving forces behind the transport of macroplastics on land are assumed to be gravity, wind, rain and surface runoff. However, the underlying transport principles remain undescribed and unresolved. To determine the minimum wind velocities, rainfall, and surface runoff that are required to mobilize and transport macroplastic items, physical laboratory experiments on an artificial hillslope were performed. Four types of macroplastic waste items were used (bottles, cups, food packaging, and bags) while surface roughness (concrete versus grass) and slope angles (0°, 10°, 20°) were systematically varied. Here we present the identified wind, rain and surface runoff thresholds, as well as the relations between the wind velocity and the plastic transport velocity. These thresholds and relations can be implemented in terrestrial plastic transport models to forecast the transport and (re)distribution of macroplastic waste on land due to wind, rain and surface runoff. The overland pathways simulated by these models, reveal where macroplastic retention occurs on land, and where terrestrial macroplastics enter waterbodies. The locations of the terrestrial accumulation zones, and the main entry points into waterbodies are crucial input for the design of mitigation and prevention measures.
Tektonika, the new Diamond Open Access journal for structural geology and tectonics
Authors: Fernández-Blanco David , Gouiza Mohamed , Bond Clare , McCarthy Dave , Lee Amicia , Pérez-Díaz Lucia ,   Tektonika DOAJ community
Published: March 2022

Tektonika is a new community-led diamond open-access journal (DOAJ) for structural geology and tectonics. It will join the growing body of DOAJs established over recent years and expand the open science movement within academia. Tektonika is a grass-roots initiative, driven by the enthusiasm and devotion of a wide and diverse spectrum of Earth Scientists from around the globe. This project endeavors to set up an open platform, devoid of any paywall, to publish, disseminate, and promote structural geology and tectonics research, and shape, alongside the other initiatives, the bright future of open access publishing.

This contribution comes shortly after the official kick-off date of Tektonika and is a great opportunity to provide an overview of our experience setting up a DOAJ and highlight the milestones that have been achieved. These include the selection of executive and associate editorial teams, as well as journal policies regarding submissions, peer-review, article types, journal scope, ethos, and relevant guidelines. A shop with journal merchandise has been set to support the journal costs and may also fund other activities, such as mentoring programs, awards, and seminars. Most importantly, Tektonika is currently welcoming submissions as the first diamond (free-to-publish, free-to-read) open-access platform in the fields of tectonics and structural geology.
Convective organization from the interaction between cold pools and thermally induced mesoscale circulations
Authors: Engelbrecht Edward , Haerter Jan
Published: March 2022

Convective organization from the interaction between cold pools and thermally induced mesoscale circulations

Land surface conditions can influence the onset and strength of convective systems. This is because sharp gradients in land surface properties result in the development of mesoscale atmospheric circulations that initiate convection, similar to the circulations present in land-sea breezes (Dirmeyer, 1995). The influence of these mesoscale circulations on convection has been well established, however, moist convection can also alter the circulations that led to its initiation - an aspect which has largely been neglected in the literature (Rieck et al., (2015)). An implication that follows from the effect of moist convection on the circulations is that it is possible for the surface temperature gradient (which triggers the initial mesoscale circulation) to reverse under cold pool effects, thus reversing the direction of the mesoscale circulation in order to bring rain to conditionally unstable regions (such as wet soils). Under these conditions, how does the mesoscale circulation tie in with cold pool outflow to organize convection across a domain with heterogeneous surface properties?

Motivated by the modeling framework from previous studies (e.g. Huang and Margulis, 2012; Rieck et al., 2015; Schneider et al., 2019)  we employ a checkerboard of alternating extremely dry vs. wet soil moisture patches in an idealized cloud resolving model coupled to a land surface model. The checkerboard approach has previously been used to show how the PBL, cloud size, precipitation duration and amount are all influenced by different strength and length scales of the land-surface gradients. Using this setup, mesoscale circulations induced by surface heterogeneities are therefore overlaid with cold pool circulations from subsequent afternoon convection. Our aim is to investigate the role of the interactions between thermal mesoscale and cold pool circulations to organize diurnal convection in a way that allows upscale growth of scattered convection in an environment where background wind is absent. Originating from this we aim to understand 1) whether convection over wet patches is determined by convection first triggering over dry patches, 2) What are the conditions required so that the static thermal circulation (controlled by the soil moisture discontinuity) will change direction? 3) To what extent does the interaction between cold pool outflow and mesoscale circulations maintain the initial soil moisture gradient field in the absence of background wind? These questions may help answer how land use changes (due to deforestation, agriculture etc.), and the resultant circulation changes focus convection so that rainfall becomes locally more extreme.
The hidden power of lightning: studying the most explosive events in thunderstorms with the world’s largest cosmic-ray observatory
Authors: Colalillo Roberta , Dwyer Joseph ,   Pierre Auger Collaboration
Published: March 2022

The Pierre Auger Observatory, designed to study ultra-high energy cosmic rays, has accidentally observed, with its 3000 km2 surface array of water-Cherenkov detectors, several events that are very likely downward TGFs. Their morphology, as well as the signals observed in the detectors, are totally different from what observed when an extensive air shower strikes the array. The TGF-like events are characterized by large footprints (~200 km2) and long signals (~ 10 µs), if compared to cosmic-ray showers. They happen in coincidence with lightning as demonstrated by the correlation with WWLLN data and with strong variations observed in the electric fields measured by the E-mills available at the Observatory. Other events within 1 ms of these peculiar events were observed in the same zone of the array. This time interval is about the time taken by the steeped leader to reach its full length. Finally, from a first reconstruction, the source altitude of this events is estimated to be very close to ground, at about 1-2 km. From lidar measurements, we know that there were low clouds at altitudes compatible with the estimated source location at the time of many events. The source altitudes can be used as input parameter for the REAM simulation to start a campaign to compare our experimental results with TGF models. The rate of TGF-like events per year is very low, less than 2 events per year. To increase the statistics, a modification to the read-out logic to give priority to events which contain long signals was implemented ans is under test.
Multi-method based characterization of calving events at Salajiegna glacier - Northern Sweden
Authors: Schulthess Martin , Kirchner Nina , Sigray Peter
Published: March 2022

Sea level rise concerns millions of people in coastal areas across the globe. One of the largest uncertainties to project future sea level rise is the frontal ablation (accounting for calving and submarine melt) at marine ice margins, around the Greenland and Antarctic Ice Sheet. High rates of frontal ablation have been observed to imply, through loss of the buttressing effect but not limited to it, increased mass loss from marine terminating glaciers and hence, associated sea level rise. This study focuses on calving processes at a freshwater lake in northern Sweden, which represents a simpler environment to study calving processes than the marine one, because impacts of tides, salinity, and circulation (all known to be relevant at marine ice-ocean boundaries) can be neglected. A multi-method approach to quantify and characterize calving events is presented here, exploring and analysing the underwater acoustic soundscape at a calving glacier front, in connection with optical, image-based methods such as timelapse photography, and photogrammetry based on footage acquired by an uncrewed aerial vehicle (UAV). An acoustic detector is developed, tested and applied to a data set acquired during 2020, and results indicate that the acoustic detector can be an important complement in the range of tools used to observe, and quantify, calving. Applied in remote locations, where continuous monitoring is difficult and where optical methods are of limited use, collecting acoustic data and monitoring calving by means of its acoustic signature could render insights previously not available (because of lacking data and methodology).
Evaluation of the COSMO model in polarimetric radar space – impact of uncertainties in model microphysics, retrievals and forward operators
Authors: Shrestha Prabhakar , Mendrok Jana , Pejcic Velibor , Trömel Silke , Blahak Ulrich , Carlin Jacob
Published: March 2022

Polarimetric observation operators generate virtual observations from the models, which enable a direct comparison of observed and simulated radar signatures of microphysical processes. However, differences in polarimetric fingerprints between observations and models may result both from model deficiencies and faulty assumptions in observation operators. Using the Bonn Polarimetric Radar forward Operator (B-PRO), the evaluation of the German weather forecast model COSMO in radar observation space revealed deficiencies in the ice-snow partitioning and spurious graupel production near the melting layer. Follow-up sensitivity experiments with the model and forward operator (FO) guided the improvement of model parameters, namely the critical diameter of particles for ice-to-snow conversion by aggregation (Dice) and the threshold temperature responsible for graupel production by riming (Tgr), pushing the synthetic radar variables closer to the observations. However, the model still exhibited a low bias (lower magnitude than observation) in simulated polarimetric moments at lower levels above the melting layer (  -3 to   -13 ° C), where snow was found to dominate. Sensitivity experiments with the FO also could not explain this bias indicating shortcoming in the FO or missing cloud microphysical processes in the 2-moment cloud microphysical scheme of the model.
Evaluating the hydrological utility of four gridded precipitation datasets for streamflow prediction in a mountainous basin
Authors: Hafizi Hamed , Sorman Ali Arda
Published: March 2022

Precipitation with high spatio-temporal resolution is one of the critical components of meteorological forcing for hydrological modeling. Its accurate measurement required a large number of rain gauges that are limited for many regions, especially highly elevated domains with a complex and mountainous topography, such as the eastern part of Turkey. On the other hand, open access Gridded Precipitation Datasets (GPDs) varying in spatial and temporal resolutions deliver alternative sources in data-scarce regions. However, their hydrological utilities are to be assessed in different basins to make adequate knowledge for both the developers and end-users. Hence, this study was carried out to investigate the spatio-temporal stability and hydrological utility of four GPDs (MSWEPv2.8, CHIRPSv2.0, ERA5, and IMERGHHFv06) over the upper Euphrates (Karasu) River Basin in the eastern part of Turkey. The accuracy of selected GPDs compared to observed precipitation is expressed in the form of Kling–Gupta Efficiency (KGE), while Hanssen–Kuiper (HK) skill score was utilized to address the detectability strength of GPDs for five different precipitation intensities. Moreover, the hydrological utility of GPDs is evaluated by employing a conceptual hydrologic model under KGE and Nash–Sutcliffe Efficiency (NSE) statistical metrics. Overall, MSWEPv2.8 shows the highest performance (median KGE of 0.34) for the direct comparison with observed precipitation followed by CHIRPSv2.0 (median KGE of 0.34) and ERA5 (median KGE of 0.08) where IMERGHHFv06 shows low performance (median KGE of 0.02) comparatively. Furthermore, CHIRPSv2.0 shows a stable performance for streamflow prediction compared to other Gridded precipitation datasets for the entire period (2015 – 2019), considering two different scenarios. These findings provide guidance for selecting appropriate GPD for the particular region of interest.
Filling in the Gaps: Consistently detecting previously unidentified extreme weather event impacts
Authors: Schwarz Moritz , Pretis Felix
Published: March 2022

Existing databases for extreme weather events such as floods, heavy rainfall events, or droughts are heavily reliant on authorities and weather services manually entering details about the occurrence of an event. This reliance has led to a massive geographical imbalance in the likelihood of extreme weather events being recorded, with a vast number of events especially in the developing world remaining unrecorded. With continuing climate change, a lack of systematic extreme weather accounting in developing countries can lead to a substantial misallocation of funds for adaptation measures. To address this imbalance, in this pilot study we combine socio-economic data with climate and geographic data and use several machine-learning algorithms as well as traditional (spatial) econometric tools to predict the occurrence of extreme weather events and their impacts in the absence of information from manual records. Our preliminary results indicate that machine-learning approaches for the detection of the impacts of extreme weather could be a crucial tool in establishing a coherent global disaster record system. Such systems could also play a role in discussions around future Loss and Damages.
Atmospheric blocking and the contribution of transient waves to its onset, decay and maintenance 
Authors: Suitters Charlie , Martinez-Alvarado Oscar , Hodges Kevin , Schiemann Reinhard , Ackerley Duncan
Published: March 2022

A full understanding of the dynamical behaviour of atmospheric blocking is still lacking, despite the influence of blocking towards hazardous mid-latitude weather extremes. Using geopotential height anomalies, relative to the zonal mean flow and persistent longitude-dependent eddies, and an objective feature tracking algorithm, the climatologies and lifecycles of anticyclones that contribute to blocking events are explored. Case studies and a climatology for blocking are presented using this process, and results show that this method performs favourably in relation to existing block detection methods since most blocking configurations are successfully detected. Then, blocking events are classified according to location of occurrence and persistence, and characteristics including intensity and areal extent are examined. The anticyclonic features contributing to blocks are also studied in terms of their genesis and lysis regions, along with anomaly strength and speed. It is found that many of the anticyclonic features that enter a block form a long way upstream, before travelling along the Rossby wave guide and intensify in the block. Furthermore, anticyclonic features that leave a block can then proceed to re-intensify further downstream and be part of a further blocking episode in a new location. This shows that there is an inherent interaction between transient waves and stationary blocks, and these results provide evidence for the previously-proposed selective absorption mechanism (SAM) for block maintenance.
Advancing regional scale inundation and exposure forecasting to support decision-making, and action in an African context. 
Authors: O'Shea Thomas , Neal Jeff , Hawker Laurence , MacLeod David , Otieno George
Published: March 2022

Dissemination of early warning information and effective preparedness are critical components in flood risk management and shape the dynamics of any successful early response. To enable efficient preparedness and an early response to hazards, early warning information should be simple, usable, and deployed through trusted sources.

Successful management of floods are therefore dependent on clear and systematic communication structures which are, in turn, necessary to enable dissemination of such information. However, in many parts of Africa, flood event response is hampered by a lack of information on the inundation and potential exposure, with most large-scale systems limited to river flow forecasting, while local systems may lack coverage.

In collaboration with the UK Foreign and Commonwealth Office, we have previously developed a method for fluvial flood inundation and exposure forecasting that combines hydrological forecasts from the Global Flood awareness systems (GLOFAS) with the LISFLOOD-FP global flood model. This was deployed in Mozambique to provide probabilistic inundation maps and exposure estimates to assist humanitarian response for cyclones Idai, Kenneth and Eloise.

In this work, the flood models built with globally available datasets, and models augmented with local information, were evaluated for a series of uses and cases. Specifically, the 2020 fluvial flood event in the Nzoia basin in Kenya, the 2019 tropical cyclone flooding in Mozambique from cyclone Idai and recent pluvial flooding in the Zambian city of Lusaka.

We discuss the potential and limitations of such information to inform efficient action and build resilient futures via co-production of forecasts in Kenya and the community learning lab component of the FRACTAL+ initiative in Lusaka.
Decoupling between carbon and nitrogen mineralization driven by selective microbial use of nitrogen-rich organic matter: a synthesis
Authors: Hicks Lettice
Published: March 2022

Soil microorganisms are responsible for the long-term sustainability of terrestrial ecosystems, due to their role in breaking down organic matter, which regulates the soil-atmosphere carbon balance and the release of plant-limiting nutrients, such as nitrogen and phosphorus, to fuel productivity. It is normally expected that the mineralization of carbon and nutrients should be linked via their shared origin from soil organic matter. However, it has recently been found that this is not always the case, with the potential for carbon and nutrient mineralization rates to become “decoupled” if microorganisms selectively target specific components of organic matter to avoid resource limitation. I will synthesize the results from a series of studies conducted in nitrogen-poor artic and temperate ecosystems, where we found evidence for selective microbial use of nitrogen-rich organic matter (microbial “nitrogen-mining”) which led to a decoupling of soil carbon mineralization and gross nitrogen mineralization. In arctic soils, microbial nitrogen-mining was observed in response to both simulated rhizodeposits in the laboratory, as well as nitrogen-fertilization and litter-addition treatments in the field. In the laboratory studies, nitrogen-mining was associated with a strong stimulation of microbial growth, despite a reduced microbial growth efficiency, which may be explained by the need for microorganisms to respire away excess carbon from soil organic matter in order to maintain stochiometric balance when increasing their acquisition of nitrogen. In nitrogen-poor coniferous forest soils, microorganisms also preferentially targeted more microbially-processed nitrogen-rich organic matter with a low carbon quality, leading to carbon and nitrogen mineralization that was unlinked to the average carbon-to-nitrogen ratio of the soil organic matter. Together, these studies highlight the potential for carbon and nitrogen mineralization rates in soils to be decoupled, driven by microbial demand for growth-limiting nutrients.
Fractionation of stable rhenium isotopes in terrestial hydrothermal systems
Authors: Wang Wenhao , Dickson Alexander , Dellinger Mathieu , Burton Kevin , Clark Deirdre , Eggertsson Guðjón Helgi , Einarsdóttir Íris Eva , Hilton Robert , Ingimarsson Heimir , Mesfin Kiflom Gebrehiwot , Prytulak Julie
Published: March 2022

Rhenium (Re) is a redox-sensitive element. Recent advances in the precision of measurement of the stable isotopic composition of Re (δ187Re) allow exploration of its potential as a proxy for paleoredox and/or chemical weathering [1]. However, as yet, there have been few studies reporting the geochemical cycling of Re and stable Re isotopes in the modern environments [2] [3], and processes that regulate the Re isotope behavior in hydrothermal systems remain unexplored.

Here we present results of the analysis of Re concentration and δ187Re (relative to NIST3143) for water samples collected from hydrothermal and groundwater systems in Iceland. We show that Re in basalt-hosted boiled hydrothermal fluids from Hellisheidi, Nesjavellir, Reykjanes and Svartsengi sites is isotopically heavier (δ187Re = –0.01 to +0.32‰) than Re in Icelandic basalts (δ187Re = ~–0.32‰). The direction of fractionation holds regardless of types of fluid reservoir (meteoric vs. seawater), and is consistent with precipitation of isotopically light sulfides in the hydrothermal system and/or kinetic fractionation of Re during degassing. By contrast, Re in cold (< 10°C) groundwaters collected from the Mývatn area is isotopically indistinguishable from host basalt. Natural hot spring waters exhibit variable δ187Re values (–0.28 to +0.26‰), likely reflecting mixing between hydrothermal and groundwater endmembers. The relatively isotopically heavy δ187Re from hydrothermal sources has the potential to modify the oceanic budget, which has implications for the isotope mass balance of Re.

[1] Dellinger et al. (2020) JAAS, 35, 377. [2] Dickson et al. (2020) GCA, 287, 221-228. [3] Dellinger et al. (2021) EPSL, 573, 117131.
Current geodynamics and evolution of Tjörnes transform zone, North Iceland
Authors: Bogoliubskii Viacheslav , Dubinin Evgeny , Grokholsky Andrey
Published: March 2022

Tjörnes transform zone (TFZ) is complicated fracture zone in North Iceland connecting Kolbeinsey ridge and Northern rift zone of Iceland. It includes several different structures such as segmented oblique rift, amagmatic rifts and oblique slip fault zones. They developed consequently since ca. 9 Ma. The aim of this work is to determine current geodynamic activity and ratio of tectonic and magmatic activity of each structure and adjacent structures of Mid-Atlantic ridge (MAR) basing on normal faults morphometric parameters and to reconstruct evolution of TFZ by physical modelling. Morphometric analysis is based on multibeam bathymetry data of Marine and Freshwater Research Institute in Iceland and ArcticDEM digital elevation model. There were collected data on more than 900 normal faults on five parameters: heave, thrust, length, distance between faults and maximum profile curvature. They reflect recent rate of horizontal and vertical deformations and morphological age of the normal fault. Heave and distance ratio shows the relative intensity of tectonic and magmatic activity. The results show that structures have different level of recent tectonic activity and therefore, are on different stages of their evolution. In addition, they have various tectono-magmatic ratio that proceeds from their development stage, width of faulting zone and mantle structure. Physical modeling is based extending setting with mineral oil that have numerical resemblance with oceanic crust in density, shear modulus and thickness. Two-layered model have elastic bottom layer, brittle top one and local heating source corresponding to Icelandic plume impulses. Initial configuration reflects two spreading segments of MAR that develop transform zone in conditions of crust thinning in direction out of Icelandic plume center. In result of their interaction is generation of overlapping spreading centers. One of them became extinct and another one develops into transtensive transform zone, which corresponds to Husavik-Flatey oblique slip fracture zone (HFFZ) and adjacent amagmatic rift. Activation of local heating source rejuvenates extinct branch of the overlap and generates subparallel to extension direction rifting fractures reconstructing Grímsey oblique rift with high magmatic activity. HFFZ activity abruptly declines. In conclusion, consequent development, activation and decline of structures correctly correlate with results of morphometric analysis and reflect the development stages of each structure. The specific current structure of TFZ is determined by initial development of overlapping spreading centers and their control by Icelandic plume magmatic impulses.
Reducing the energy demand to achieve universal access to modern energy while ensuring women’s well-being: How much energy and carbon can be saved by fertility decline? 
Authors: Belmin Camille , Pichler Peter-Paul , Marois Guillaume , Pachauri Shonali , Weisz Helga
Published: March 2022

In a climate-constrained world, understanding the energy needs to reach universal access to modern energy is critical. This requires making assumptions on future population trajectories, and developments in energy access can affect them. Yet, this feedback has never been accounted for in energy models. Access to modern energy enhances women’s ability to make reproductive choices and leads to fertility decline as it reduces child mortality, improves health, increases women’s access to information, education and employment. In this paper, we assess the household energy requirements to expand energy access while considering the relationship between energy access and fertility, using Zambia as a case study. To do so, we built a micro-simulation model of population projection in which fertility depends on access to modern energy and education level, and projected the electricity and cooking energy needs of the Zambian population to 2050, under different scenarios. Our preliminary results show that while electricity consumption is higher in the universal access scenario compared to the baseline scenario, total energy demand is 67% lower, partly due to strong decline in the use inefficient traditional cooking fuels. Reduced population growth due to expanded energy access and education accounts for 15% of this reduction in rural areas, and 8% overall. Although the challenge of achieving universal access to modern energy seems daunting, our results suggest that this goal could be co-beneficial to achieving climate goals. Our study also reveals that accounting for the energy-population nexus in energy models would scale down the currently assumed energy needs to ensure decent well-being for all.
Maximizing the use of remote sensing to quantify Greenland ice sheet runoff 
Authors: Sandberg Sørensen Louise ,   4DGreenland team
Published: March 2022

Our ability to monitor and quantify ice sheet runoff is essential for a better understanding of the hydrology of the Greenland Ice Sheet and its contribution to global sea-level rise in a future warming climate. The amount of liquid water at the surface of the Greenland Ice Sheet has particularly increased due to large regional warming that this region has experienced over the last decades.

Here, we present the initial results of monthly runoff estimates from the Watson drainage basin in West Greenland, developed within the 4DGreenland project. 4DGreenland is a 2-year project funded by  the European Space Agency (ESA), which is focused on quantifying and analyzing the dynamic variations in the hydrological components of the ice sheet, while maximizing the use of Earth Observation (EO) data. 

The basin scale runoff estimate is derived from adding each component of the hydrological cycle: We have used a Random Forest approach (Supervised Learning algorithm) to map supraglacial hydrology ice sheet wide from optical imagery, and used ICESat-2 derived lake bathymetry as calibration to derive storage volumes. To map meltwater we have developed algorithms for generating maps of surface melt extent from high-medium resolution C-band backscatter measurements, and Low resolution PMW data. The subglacial melt is inherently difficult to monitor. The melt produced by friction heat though is derived from a model run of an ice sheet model (Elmer/Ice) which is tuned to assimilate observed ice velocities. Lastly, we have further developed a firn model to quantify the amounf of the meltwater that is retained in the snow/firn column.

By adding these component we are able to present monthly estimated of runoff from the drainage basin, and specifically show how each of the hydrologically components change over time. 
Stochastic downscaling of the 2m temperature with a generative adversarial network (GAN)
Authors: Langguth Michael , Gong Bing , Ji Yan , Amirpasha Mozaffari , Mache Karim , Schultz Martin G.
Published: March 2022

Inspired by the success of superresolution applications in computer vision, deep neural networks have recently been recognized as an appealing approach for statistical downscaling of meteorological fields. While further increasing the resolution of numerical weather prediction models is computationally very expensive, statistical downscaling models can accomplish this task much cheaper once they have been trained.

In this study, we apply a generative adversarial network (GAN) to downscale the 2m temperature over Central Europe where complex terrain introduces a high degree of spatial variability. GANs are considered superior to purely convolutional networks since the model is encouraged to generate data whose statistical properties are similar to real data. Here, the generator consists of an u-shaped encoder decoder network which is capable of extracting features on various spatial scales. As a quasi-realistic test suite, we map data from the ERA5 reanalysis dataset onto a 0.1°-grid with the help of short-range forecasts from the Integrated Forecasting System (IFS) model. To increase the complexity of the downscaling task, the ERA5 reanalysis data is coarsened beforehand onto a 0.8°-grid, thus increasing the downscaling factor to 8. We evaluate our statistical downscaling model in terms of several evaluation metrics which measure the error on grid point-level as well as the quality of the downscaled product in terms of spatial variability and produced probability function. We also investigate the importance of static and dynamic predictors such as the surface elevation and the temperature on different pressure levels, respectively. Our results motivate further development of deep neural networks for statistical downscaling of meteorological fields. This includes downscaling of other, inherently uncertain variables such as precipitation, operations on spatial resolutions at kilometer-scale and ultimately targets an operational application on output data from global NWP models.
Estimating states and parameters in earthquake sequence models in the presence of a parameter bias
Authors: Banerjee Arundhuti , van Dinther Ylona , Vossepoel Femke
Published: March 2022

Forecasting earthquake occurrence is a challenging endeavor, which will ultimately require a combination of observations and physics-based models. Data assimilation may help to combine these and their uncertainties in a statistically solid manner. To understand the potential of ensemble data assimilation, we investigate whether the fault stress state can be estimated and forecasted in the presence of a bias in a friction parameter. In a perfect model test, we introduce different degrees of bias in rate-and-state parameter b. b describes the evolution of frictional strength with fault slip velocity and thus impacts earthquake slip and the subsequent recurrence interval. Our forward model is a simplified, zero-dimensional (0D) Burridge-Knopoff spring-block system with a rate- and state-dependent friction formulation using a ‘slip law’. We assimilate synthetic observations of fault shear stress and slip rate variables and corresponding large uncertainties. We compare state estimation with joint state-parameter estimation using a sequential importance resampling particle filter by evaluating the quality of the estimated fault stress probability density functions (pdf’s).

The results of the study indicate that state estimation works well for systems with low (3%) to intermediate (15%) bias. This performance for the case of intermediate bias can be improved through increasing model error combined with double resampling in the particle filter. For a large friction-parameter bias (42 %), we show that state-parameter estimation is the only way to correct the bias. This is an important result, because it shows that state-parameter estimation is able to identify trade-offs and separate error contributions coming from stress state and friction parameters.  Furthermore, the results of this study can be applied to other data assimilation applications involving models that are particularly vulnerable to parameter biases.
An early warning system of fire danger for the Brazilian Pantanal
Authors: Nunes Sílvia A. , Belém Liz B. C. , Libonati Renata , DaCamara Carlos C.
Published: March 2022

The devastating fires that in 2020 burned more than 3.9 million hectares in the Brazilian Pantanal, the largest tropical wetland in the world, were the result of a complex interplay among human activity, landscape characteristics, and meteorological conditions, and have deepened the concerns about the future of that unique region of the world.

The meteorological component has played a prominent role in 2020, Pantanal having been affected by the most extreme drought since 1950 and by long periods of extremely high temperature. Both factors, combined with fire ignitions, mostly related to human activities, have contributed to the onset of large fire events that spread over water-stressed vegetation.

The aim of the present work is to set up a statistical model that is able to provide reliable forecast of probability of occurrence of a wildfire, taking into consideration both the longer and shorter effects of atmospheric conditions on vegetation stress and, provided an ignition has occurred, on the building up and spreading of a wildfire.

Fire data cover the period 2001-2020 and consist of Fire Radiative Power (FRP) as acquired by the MODIS instrument on-board Aqua and Terra Satellites. Meteorological fire danger was characterized by the Fire Weather (FWI) data covering the same period from the Copernicus Emergency Management Service.

Statistical models used in this study combine a lognormal distribution central body with a lower and an upper tail, both consisting of Generalized Pareto (GP) distributions, and daily FWI is used as a covariate of the parameters of the lognormal and the two GP distributions. First a base model (with fixed parameters) is fitted to the decimal logarithm of FRP, and the quality of fit is assessed using an Anderson-Darling test. Then the model is improved using FWI as a covariate, and performances of models without and with covariate are compared by computing the Bayes Factor as well as by applying the Vuong’s closeness test.

Statistical models were developed for the nine hydrological subregions of Pantanal using data for the period 2001-2019. Five classes of meteorological fire danger were then defined based on probabilities of exceedance of predefined values of FRP. The procedure was then separately applied to the extreme year of 2020.

The developed procedure is on the basis of an operational early warning system of fire danger in Pantanal that is currently being set up.

 

This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project FIRECAST (PCIF/GRF/0204/2017), and by the State Public Prosecutor's Office of Mato Grosso do Sul.
Impact of granulated mixed liming fertilizer to soil plant available P content
Authors: Tõnutare Tõnis , Veinberg Evelin , Tõnutare Tõnu , Raave Henn , Astover Alar , Toomsoo Avo
Published: March 2022

The phosphorus is an essential element for plant growth. Therefore the pools of plant available P in soil are very important. From the environmental viewpoint, the high level of easily soluble P compounds in soil is a source of risk for leaching and contamination of waterbodies. Also, problems with depleting stock of phosphatic rock ores is increasingly on the agenda.  Approximately 80% of mined phosphatic rocks are used for producing fertilizers and the peak of mining is estimated in 20-30 years. Therefore, it is very important to find other sources for phosphorus fertilizer production.
The intensive use of mineral fertilizers causes acidification in soils. To overcome the soil acidification, liming is used. Typically, limestone and chalk are used as liming agent. During several decades also many types of ashes are used for liming agriculturally used fields. In Estonia the oil shale fly ash is used as liming agent already from 1970-s. In 2020 5,1 million tons of oil shale ash as a byproduct was produced in Estonian power plants. The powdered fly ash is a troublesome material for transportation, storage and sowing. The granulation of fly ash helps to overcome these difficulties. 
To give the added value to the granulated liming agent, the plant nutrient can be mixed into source material. For modifying granulated ashes also byproducts and residues of some other processes can be used. In our research, the oil shale ash granules were modified by using different ratios of biochar and bone meal. For the comparison, wooden ash with same impurities was used. In pot experiment, different doses of fertilizer were used. The changes in plant available P and pH in soil during 12 month in soil were investigated
Local Physical Chemistry Statements for Low cost sensor added value
Authors: Scheffler Janice , Vieno Massimo , Flynn Michael , Lacy Stuart , Diez Sebastian , Pope Francis D. , Edwards Pete , Braban Christine F.
Published: March 2022

Air quality monitoring with a high spatial and temporal resolution is essential to understand the sources, processes, and impacts of air quality (AQ) on human health and the environment, especially in densely populated urban areas. Current state-of-the-art instruments don’t allow for such a high spatial resolution AQ monitoring due to costs. Low cost sensors (LCS) provide an opportunity to bridge this divide. There is a large volume of research papers assessing the performance of LCS for particulate matter (PM) against reference PM instrumentation.  Across most studies, a general observation is that LCS PM sensors as offered by the commercial and research markets have an inherent problem with accuracy at high relative humidities and for varying PM composition.

This study uses measured and modelled chemical composition of PM2.5  at the  Manchester UK urban supersite to addresses the underlying physical chemistry which leads to the mass of the measured PM. Measurements from LCS and reference PM instruments co-located with state-of-the-art research chemical composition and meteorology. To calculate the composition of PM2.5, we used XACT for metals, the ACSM for salt ion concentrations, and an Aethalometer for Black Carbon. From this, the LCSs variability in data can be understood.  We present a comparison of LCS PM2.5 concentrations with measured and calculated total PM2.5 concentrations from reference instruments for time periods with different Air Quality characteristics and discuss the physico-chemical characteristics leading to varying results of low cost PM sensors. The future research will apply methodology to modelled UK wide data and develop tools for non specialist users to understand LCS in terms of local air pollution and specifically PM2.5 chemical composition and meteorology.