bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping Antarctic Crevasses at High Spatiotemporal Resolution with Deep Learning applied to Synthetic Apertur Radar Data
Authors: Surawy-Stepney Trystan , Hogg Anna E. , Cornford Stephen L. , Hogg David
Published: February 2023

Understanding how the presence of fractured ice alters the dynamics, hydrology and energy balance of glaciers and ice shelves is important in determining the future evolution of the Antarctic Ice Sheet (AIS). However, these processes are not all well understood, and large-scale quantitative observations of fractures are sparse. Fortunately, the large amount of satellite data covering Antarctica gives us the opportunity to change this.The Sentinel-1 satellite cluster, from the European Space Agency's Copernicus programme, has acquired synthetic aperture radar (SAR) data over the AIS with a repeat period of 6-12 days for the last 8 years. A broad range of crevasse types are visible in this imagery: rifts, surface crevasses and some basal crevasses on ice shelves, and fine surface crevasses on grounded ice streams - even those bridged by snow or pixel-scale in width.In this study, we use machine learning to automatically map crevasses in this imagery; producing monthly composite maps over the AIS at 50m resolution. We developed algorithms to partition crevasses into those on grounded and floating ice, and extract these features in parallel using a mixture of convolutional neural networks, trained in a weakly supervised way, and more traditional computer vision techniques.Having developed parallelisable routines for the large-scale batch processing of SAR data, we have processed every Sentinel-1 acquisition over the Antarctic Ice Sheet. The resulting dense timeseries of fracture maps allows us to assess the evolution of crevasses during the Sentinel-1 acquisition period. By measuring the density of fractures we develop a method to quantify structural change on ice shelves, and investigate those of the Amundsen Sea Embayment. We show an increase in crevassing in buttressing regions of the Pine Island and Thwaites ice shelves over the last 8 years, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses.Finally, we develop methods demonstrating how our fracture data can be assimilated into numerical modelling experiments aiming to quantify the impact of ice shelf fracture on glacier dynamics.
FIELDSTONE: a computational geodynamics (self-)teaching tool
Authors: Thieulot Cedric
Published: February 2023

The 'Fieldstone' project is an attempt to first and foremost provide the Utrecht University students with a single source of knowledge/information about the field of computational geodynamics. It consists of a unique blend of multiple syllabuses for the geophysics courses that I teach, more than a hundred of educational python codes covering many topics (finite elements, finite differences, rheology, mass momentum energy equations, plotting, some data processing, etc ...) and a rudimentary attempt at organising more than 4000 references by topic. It is an ongoing open source project that is constantly updated by myself with occasional contributions by our MSc students. It is however not only meant for students as it is also used by colleagues and collaborators of different universities for personal study or to train their own students. In my presentation I will explain the philosophy and structure of the project, show examples of some of the key educational codes and reflect on how I see this project mature in the coming years and hopefully find its audience. 
New Insights on the seismic unrest of La Palma 2021 eruption through a purely automatic analysis
Authors: Díaz-Suárez Eduardo Andrés , Dominguez Cerdeña Itahiza , Del Fresno Carmen , Villaseñor Antonio , Sainz-Maza Aparicio Sergio
Published: February 2023

We have analyzed the seismic series that preceded La Palma 2021 eruption using a purely automatic methodology based on Deep Learning. A new catalog has been retrieved, firstly 5797 absolute locations were obtained using NLLoc algorithm and considering a 3D velocity model of the island. At a further stage, these results were improved computing relative locations with HypoDD code. Our final catalog has doubled the number of events of the original manual catalog of the Instituto Geográfico Nacional. We observe new detailed features of the seismicity migration throughout the crust. We can clearly differentiate two phases within the sequence: the first one from September 11th to 16th and the second one from September 16th until the eruption onset. This differentiation can be described in terms of independent seismic parameters: inter-event time dispersion coefficient, b-value spatio-temporal distribution, hypocentral migration, magnitude distribution). Our results are in agreement with previous geodetic studies. Finally, we have developed a conceptual model describing the magmatic unrest and how the magma made its way to the surface prior to this eruption. 
Spin-up strategy for ocean biogechemistry in a high resolution Earth System Model
Authors: Chegini Fatemeh , Casaroli Lucas , Salinas Mariana , Nielsen David , Maerz Joeran , Mathis Moritz , Hülse Dominik , Ramme Lennart , Li Hongmei , Ilyina Tatiana
Published: February 2023

Increasing computational power enables Earth System Models (ESMs) to be run at higher resolutions than on conventional grids with spacings of O(100km). The new generation of ESMs running at resolutions of O(5-10km) are able to resolve phenomena such as mesoscale eddies in the ocean and convective storms in the atmosphere. Resolving these features can be a step towards reducing uncertainties in carbon cycle modeling as they directly affect the ocean uptake of anthropogenic carbon (Harrison et al. 2018). However, the spin-up of ocean biogeochemistry in globally high resolution ESMs remains computationally challenging. Traditionally, the spin-up duration of ocean biogeochemical components (e.g., in CMIP5/CMIP6 models) ranges from one hundred to several thousand years to avoid model drifts in the euphotic, mesopelagic and even deeper ocean that has an overturning time of O(1000 years). This long spin-up time is, however, not yet computationally affordable in high resolution ESMs despite recent advances in improving their scalability (Linardakis et al. 2022). Therefore, different spin-up strategies are required and need to be explored.We here present our strategy to run the HAMburg Ocean Carbon Cycle model (HAMOCC; Ilyina et al. 2013, Jungclaus et al. 2022) in the high resolution ICON-Sapphire ESM (Hohenegger et al. 2022) configuration. We discuss the steps we take from tuning and spin-up of HAMOCC in a cascade of resolutions and configurations: initially in a 40km ocean only setup, subsequently in a 10km ocean-only and eventually a 5km ESM setup. Furthermore, we examine the possibility of replacing interpolated results (used as initialization for the next higher resolution) with available observations (e.g., nutrients, alkalinity, dissolved inorganic carbon) and its consequence on biogeochemical drifts of key tendencies such as CO2 surface fluxes. Finally, we discuss the scientific questions that can be addressed using this spin-up strategy and its limitations. References:Harrison et al. 2018: https://doi.org/10.1002/2017GB005751Hohenegger et al. 2022: https://doi.org/10.5194/gmd-2022-171Ilyina, T., et al. 2013: https://doi.org/10.1029/2012MS000178.Jungclaus et al. 2022: https://doi.org/10.1029/2021MS002813Linardakis et al. 2022: https://doi.org/10.5194/gmd-2022-214
Combining sea-ice and ocean data assimilation with nudging atmospheric circulation in the AWI Coupled Prediction System
Authors: Losa Svetlana N. , Mu Longjiang , Athanase Marylou , Streffing Jan , Andrés-Martínez Miguel , Nerger Lars , Semmler Tido , Sidorenko Dmitry , Goessling Helge F.
Published: February 2023

Assimilation of sea ice and ocean observational data into coupled sea-ice, ocean and atmosphere models is known as an efficient approach for providing a reliable sea-ice prediction (Mu et al. 2022). However, implementations of the data assimilation in the coupled systems still remain a challenge. This challenge is partly originated from the chaoticity possessed in the atmospheric module, which leads to biases and, therefore, to divergence of predictive characteristics. An additional constrain of the atmosphere is proposed as a tool to tackle the aforementioned problem. To test this approach, we use the recently developed AWI Coupled Prediction System (AWI-CPS). The system is built upon the AWI climate model AWI-CM-3 (Streffing et al. 2022) that includes FESOM2.0 as a sea-ice ocean component and the Integrated Forecasting System (OpenIFS) as an atmospheric component. An Ensemble-type Kalman filter within the Parallel Data Assimilation Framework (PDAF; Nerger and Hiller, 2013) is used to assimilate sea ice concentration, sea ice thickness, sea ice drift, sea surface height, sea surface temperature and salinity, as well as temperature and salinity vertical profiles. The additional constrain of the atmosphere is introduced by relaxing, or “nudging”, the AWI-CPS large-scale atmospheric dynamics to the ERA5 reanalysis data. This nudging of the large scale atmospheric circulation towards reanalysis has allowed to reduce biases in the atmospheric state, and, therefore, to reduce the analysis increments. The most prominent improvement has been achieved for the predicted sea ice drift. Comprehensive analyses will be presented based upon the new system’s performance over the time period 2003 – 2022.Mu, L., Nerger, L., Streffing, J., Tang, Q., Niraula, B., Zampieri, L., Loza, S. N. and H. F. Goessling, Sea-ice forecasts with an upgraded AWI Coupled Prediction System (Journal of Advances in Modeling Earth Systems, 14, e2022MS003176. doi: 10.1029/2022MS003176.Nerger, L. and Hiller, W., 2013. Software for ensemble-based data assimilation systems—Implementation strategies and scalability. Computers & Geosciences, 55, pp.110-118.Streffing, J., Sidorenko, D., Semmler, T., Zampieri, L., Scholz, P., Andrés-Martínez, M., Koldunov, N., Rackow, T., Kjellsson, J., Goessling, H., Athanase, M., Wang, Q., Sein, D., Mu, L., Fladrich, U., Barbi, D., Gierz, P., Danilov, S.,  Juricke, S., Lohmann, G. and Jung, T. (2022) AWI-CM3 coupled climate model: Description and evaluation experiments for a prototype post-CMIP6 model, EGUsphere, 2022, 1—37, doi: 10.5194/egusphere-2022-32
Unexpectedly low organic carbon burial efficiency in anoxic sediments is linked to the absence of physical protection: lessons from the Western Gotland Basin (Batic Sea)
Authors: Placitu Silvia , van de Velde Sebastiaan , Robertson Elizabeth K. , Hall Per O. J. , Bonneville Steeve
Published: February 2023

Burial of organic carbon (OC) in marine sediments controls atmospheric CO2 and O2 concentrations and is a key process in the global carbon cycle. Marine sediments bury ~160 Tg C yr-1 globally, of which ~90% in continental shelf sediments, which makes these sites a burial hotspot. It is generally assumed that OC is buried more efficiently in sediments underlying anoxic bottom waters. Recently, however, sediments of the Gotland Basin (Baltic Sea) have been shown to have unusually low OC burial efficiencies (~10%) considering their sediment accumulation rates, despite being overlaid by anoxic bottom waters.  To investigate the reason for this lower-than-expected OC burial efficiency, we sampled five sites along a transect (including two sites for Fe-OM assessment) across the Western Gotland Basin. Sulphate reduction rate measurements showed that the OC reactivity was much higher than was expected for the age of the OC. Subsequent analysis of iron-organic matter associations and OC-to-surface area ratios showed that the potential for physical protection via either coprecipitation with iron minerals or mineral adsorption was very low (below the detection limit in one core and a maximum of 7% of OC linked to Fe in the other, which is low compared to the global average of ~20%). Our results suggest that in the Western Gotland Basin, the absence of physical protection seems to allow for unusually high mineralisation rates (low OC burial efficiency) despite anoxic conditions. Consequently, the link between oxygen exposure and OC mineralisation rate is likely not as universal as currently assumed. 
Spatiotemporal Identification and Characterization of Extreme Hydrometeorological Events Patterns in the Magdalena River Basin
Authors: Duarte Santiago , Corzo Gerald , Solomatine Dimitri
Published: February 2023

Single extreme hydrometeorological events have been highly studied around the world, however, concerns related to the spatiotemporal variations have extended the studies to look for more insight into space and time dimensions. In this context, of increasing importance are the relations of extreme events properties over multiple spatial and temporal scales. Nevertheless, the study of these relations has not been widely developed. The interaction between events, like floods and droughts with different spatiotemporal characteristics, so far, has still yet to be further studied. Some studies show that there are complex relations linking between both extremes, since occurrences of both are observed in single catchment areas around the world. Furthermore, when analyzing time and space scales for concurrent or successive events, the complexity increase. Recent advances in the spatiotemporal analysis of droughts and floods include tracking approaches, data-driven probabilistic models and machine learning applications. Likewise, new studies have highlighted the usefulness of data mining techniques in extracting knowledge, identifying patterns and detecting anomalies from climate databases.Therefore, the main objective of this research is to characterize and identify spatial and temporal patterns related to extreme hydrometeorological events generation and propagation using data mining techniques. The selected case study is the Magdalena River basin in Colombia. This basin produces most of Colombia’s Gross Domestic Production (GDP), which is highly dependent on the water resource. Because of this, extreme hydrological events such as floods or droughts have a large impact all over the basin.ERA5-Land information (precipitation, temperature, surface pressure and wind U and V components) from 1980-2020 with a resolution of 0.1°x0.1° at multiple time scales (hourly and monthly) was collected for this study. This data was used to identify and characterize extreme hydrometeorological events for multiple time steps and indices thresholds. Temporal, spatial, climatic and geometrical properties of each extreme event region were calculated and stored in a hydrometeorological database. Unsupervised machine learning clustering algorithms (k-means, hierarchical clustering, DBSCAN and spectral) were applied on the database to cluster elements with similar property values. At last, a data mining association rules method (APRIORI) was applied to identify clear patterns between cluster elements of extreme hydrometeorological events. As a main result of this study, is expected an improved understanding of the extreme hydrometeorological events patterns and their associated hydro-climatic processes in the region. This knowledge can help to obtain more accurate and less uncertain estimations of extreme hydrological events, as these are major challenges of many water resources problems, such as monitoring and forecasting.
Environmental data value stream as traceable linked data - Iliad Digital Twin of the Ocean case
Authors: Zaborowski Piotr , Atkinson Rob , Villar Fernandez Alejandro , Palma Raul , Brönner Ute , Berre Arne , Bye Bente Lilja , Redd Tom , Voidrot Marie-Françoise
Published: February 2023

In the distributed heterogeneous environmental data ecosystems, the number of data sources, volume and variances of derivatives, purposes, formats, and replicas are increasingly growing. In theory, this can enrich the information system as a whole, enabling new data value to be revealed via the combination and fusion of several data sources and data types, searching for further relevant information hidden behind the variety of expressions, formats, replicas, and unknown reliability. It is now visible how complex data alignment is, and even more, it is not always justified due to capacity and business issues. One of the challenging, but also most rewarding approaches is semantic alignment, which promises to fill the information gap of data discovery and joins. To formalise one, an inevitable enabler is an aligned, linked, and machine readable data model enabling the specification of relations between data elements generated information. The Iliad - digital twins of the ocean are cases of this kind, where in-situ data and citizen science observations are mixed with multidimensional environmental data to enable data science and what-if models implementation and to be integrated into even broader ecosystems like the European Digital Twin Ocean (EDITO) and European Data Spaces. An Ocean Information Model (OIM) that will enable traversals and profiles is the semantic backbone of the ecosystem. Defined as the multi-level ontology, it will explain data using well known generic (Darwin Core, WoT), spatio-temporal (SOSA/SSN, OGC Geo, W3C Time, QUDT, W3C RDF Data Cube, WoT) and domain (WORMS, AGROVOC) ontologies. Machine readability and unambiguity allow for both automated validation and some translations.On the other hand, efficient use of this requires yet another skill in data management and development besides GIS, ICT and domain expertise. In addition, as the semantics used in the data and metadata have not yet been stabilised on the implementation level, it introduces a few more flexibilities of data expression. Following the GEO data sharing and data management principles along with FAIR, CARE and TRUST, the environmental data is prepared for harmonisation. Furthermore, to ease the entry and to harmonise conventions, the authors introduce a multi-touchpoint data value chain API suite with an aligned approach to semantically enrich, entail and validate data sets such as observations streams in JSON or JSON-LD based on OIM, through storage and scientific data in NetCDF to exposing this semantically aligned data via the newly endorsed and already successful OGC Environmental Data Retrieval API. The practical approach is supported by a ready-to-use toolbox of components that presents portable tools to build and validate multi-source geospatial data integrations keeping track of the information added during mesh-up and predictions and what-if implementations.
,000 years of recurrent volcaniclastic megabed deposition in the Marsili Basin, Tyrrhenian Sea
Authors: Sawyer Derek , Urgeles Roger , Lo Iacono Claudio
Published: February 2023

Megabeds, or ¨megaturbidites¨, are exceptionally large submarine deposits interpreted to originate from significant geohazard events. Megabeds result from rapid discharges of large volumes of sediments from continental margins to the deep ocean. Using high resolution 3.5 kHz subbottom profiler data, we discover four megabeds constituting ~75% of the deposits in the upper 60 meters (~ last 60 ky) of the western Marsili Basin, Tyrrhenian Sea. The megabeds are widespread and imaged as distinct acoustically transparent units with ponded geometries, 10 to 25 m thick, separated by parallel-bedded strata. Each megabed is thinner and volumetrically smaller than the proceeding one. Minimum volume estimates of 1.3, 9.4, 11.8, and 13.3 km3, respectively.  A synthetic seismogram and well tie at Site 650 of Ocean Drilling Program Leg 107 demonstrates that megabeds correspond to a thick unit of mud lying on top of a high amplitude facies of normally graded volcaniclastic silt or sand. Mud deposits are structureless, consistent with a turbidite origin, except for the transparent facies of Megabed 3, which corresponds to a highly deformed muddy debris flow. The detailed well-tie together with previous chronological constraints from volcaniclastics glass chemistry, suggest that Megabeds 3, and possibly 4 may be associated with the 39.8 ka Campanian Ignimbrite super-eruption of the Campi Flegrei caldera, Italy, described among the largest eruptions on Earth. Most likely source areas for the most recent megabeds are the volcanic provinces to the north or south, and the Stromboli canyon-channel system to the east. Surprisingly, the deposit geometries suggest the megabeds are not sourced locally from the Marsili seamount, which is the largest active volcano in Europe. Regardless of source locality, the megabeds entered the Marsili Basin rapidly as evidenced by significant basal erosion and fluid escape structures, some of which remain active. The newly discovered megabeds of the Marsili basin may indicate significant geohazard events for the circum-Tyrrhenian Sea coastlines. 
Late Maastrichtian climatic shifts and faunal upheavals at DSDP Site 525A (South Atlantic): Understand the K-Pg boundary crisis in the long-term perspective
Authors: Singh Brijesh , Punekar Jahnavi , Spangenberg Jorge , Keller Gerta
Published: February 2023

The extensively studied Cretaceous-Palaeogene (K-Pg) boundary mass extinction of planktic foraminifera (~66 Ma) has been linked with two catastrophic triggers: the Chicxulub impact (Gulf of Mexico) and Deccan volcanism (India). All the studies of the past three decades have focused on the climate shifts and faunal stress at the K-Pg boundary, and in the final ~200 ky preceding it. However, it is critical to study the events that precede the age and influence of Deccan volcanism to gain perspective on the true magnitude of the biotic crisis at the boundary. This study presents a new high-resolution (at 20-cm intervals) climate and faunal dataset of the entire late Maastrichtian record at the DSDP Site 525A (Walvis ridge, South Atlantic). The DSDP Site 525A offers a relatively continuous sediment record of the late Maastrichtian with near-pristine planktic foraminifera for faunal and stable isotopic analyses, excellent magnetostratigraphy and potential for cyclostratigraphy.            Our results reveal sediments spanning biozones CF1 through CF7 of the Maastrichtian, a duration of ~6 myr. The updated biozone boundaries have been used to infer the relative ages of the observed faunal and climate shifts. The seven important late Maastrichtian climatic events (E1-7) are clearly identified in stable isotopic (δ13C and δ18O) records of planktic (Rugoglobigerina rugosa) and benthic foraminifera (Cibicidoides cf.). Of these, events E-2, E-4 and E-6 represent relatively warmer climate, whereas events E-1, E-3, E-5 and E-7 are colder climate. Events E-2, E-4 and E-7 correspond to the globally recognized Deccan warming event, mid-Maastrichtian event (MME) and Campanian-Maastrichtian Boundary Event (CMBE), respectively. Based on isotope records, the late Maastrichtian corresponds with four climate shifts. Faunal analysis reveals these events are bracketed within four biozones (CF1-4) and have a species richness of ~50 planktic foraminifera. The census analysis reveals a slightly decreasing diversity in E-4 followed by a uniform diversity in E-3. Further, E-2 witnessed a sharp fall in the diversity trend that reduced to an all-time low of ~20%. Most planktic species (globotruncanids) get extinct in E-2 following the diversity drop. However, Hedbergella spp., Pseudoguenbelina costulata, P.  hariaensis, Heterohelix globulosa, H. rajagopalani, Globigerinella aspera, and Globotruncana arca manage to survive through E-2 and were present in high abundance (~10%). Our long-term study suggests an overall stress built up in the background that must have aided in the K-Pg boundary mass extinction.
The ALTIUS mission: status and performance
Authors: Berthelot Antonin , Baker Noel , Demoulin Philippe , Errera Quentin , Franssens Ghislain , Fussen Didier , Mateshvili Nina , Pieroux Didier , Sotiriadis Sotiris , Dekemper Emmanuel
Published: February 2023

ALTIUS (Atmospheric Limb Tracker for the Investigation of the Upcoming Stratosphere) is an atmospheric limb mission being implemented in ESA's Earth Watch programme and planned for launch in early 2026. The primary objective of the mission is to measure high-resolution stratospheric ozone concentration profiles. Secondary objectives are the retrievals of stratospheric aerosols particle density, NO2, water vapor and other minor species concentrations.This innovative instrument consists of three spectral high-resolution imagers: UV (250-355 nm), VIS (440-675 nm) and NIR (600-1040 nm) channels. The UV channel uses a stack of four Fabry-Pérot interferometers, while the VIS and NIR channels each rely on an AOTF (Acousto-Optical Tunable Filter). Each channel can image scenes independently of the others at given wavelengths and with a moderate spectral resolution, and high spatial sampling. The agility of ALTIUS allows for series of observations at desired wavelengths carefully chosen to retrieve the vertical profiles of species of interest.The instrument will perform measurements in different geometries to maximize global coverage: observing limb-scattered solar light in the dayside, solar occultations at the terminator, and stellar, lunar, and planetary occultations in the nightside.The status of the ALTIUS mission will be presented as well as the foreseen quality of the Level-1 observations.  The quality of the retrieved profile densities will be discussed with a particular focus on the high vertical resolution that can be achieved using this instrument. The added-value of the native imaging capabilities of ALTIUS in terms of observations, and in-flight calibrations, will be highlighted.
Downscaling with a machine learning-based emulator of a local-scale UK climate model
Authors: Addison Henry , Kendon Elizabeth , Ravuri Suman , Aitchison Laurence , Watson Peter
Published: February 2023

High resolution rainfall projections are useful for planning for climate change [1] but are expensive to produce using physical simulations. We make novel use of a state-of-the-art generative machine learning (ML) method, diffusion models [2], to more cheaply generate high resolution (8.8km) daily mean rainfall samples over England and Wales conditioned on low resolution (60km) climate model variables. The downscaling model is trained on output from the Met Office UK convection-permitting model (CPM) [3]. We then apply it to predict high-resolution rainfall based on either coarsened CPM output or output from the Met Office HadGEM3 general circulation model (GCM). The downscaling model is stochastic and able to produce samples of high-resolution rainfall that have realistic spatial structure, which previous methods struggle to achieve. It is also easy to train and should better estimate the probability of extreme events compared to previous generative ML approaches.The downscaling model samples match well the rainfall distribution of CPM simulation output. We use as our conditioning variables We obtained further improvements by also including high-resolution, location-specific parameters that are learnt during the ML training phase. We will discuss the challenges of applying the model trained on coarsened CPM variables to GCM variables and present results about the method’s ability to reproduce the spatial and temporal behaviour of rainfall and extreme events that are better represented in the CPM than the GCM due to the CPM’s ability to model atmospheric convection.References[1] Kendon, E. J. et al. (2021). Update to the UKCP Local (2.2km) projections. Science report, Met Office Hadley Centre, Exeter, UK. [Online]. Available: https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/ukcp18_local_update_report_2021.pdf[2] Song, Y. et al. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. ICLR.[3] Met Office Hadley Centre. (2019). UKCP18 Local Projections at 2.2km Resolution for 1980-2080, Centre for Environmental Data Analysis. [Online]. Available: https://catalogue.ceda.ac.uk/uuid/d5822183143c4011a2bb304ee7c0baf7
Future satellite gravity field missions – Impact of quantum sensors and extended satellite constellations
Authors: Zingerle Philipp , Pail Roland , Gruber Thomas , Abrykosov Petro
Published: February 2023

With the evolution of cold atom interferometry (CAI), an adaptation for spaceborne applications may become possible in the near future. One of the applications which may benefit from such CAI instruments are next-generation satellite gravity field missions (NGGMs), since they rely heavily on the accelerometer performance. Here, either future satellite-to-satellite tracking (SST) missions (such as GRACE/-FO) or satellite gravity gradiometry (SGG) missions (such as GOCE) are feasible. Until now, only electrostatic accelerometers have been used. However, all suffer from an increased long-term instability which affects the accuracy of the long wavelengths of the retrieved gravity field. In this contribution we investigate the impact of CAI sensors on various NGGM mission concepts (either SST or SGG variants) and quantify the instrument-only error separately from the full gravity field retrieval error (which is hampered by temporal aliasing). Knowing that temporal aliasing currently poses one of the main limiting factors, special attention is given to strategies which may help to minimize this error source. Therefore, in addition to investigating future instruments, also extended mission constellations containing several satellites/pairs and alternative satellite configurations are examined with respect to their time-variable gravity field retrieval performance. This work is supported by the ESA QSG4EMT study in collaboration with Politecnico di Milano, Delft University of Technology, HafenCity University Hamburg, University of Bonn and University of Trieste.
Embedding the Spatial Heterogeneity of the Agricultural Landscape into Crop Model Parameters to Refine Sub-Regional Yield Predictions  
Authors: Bajocco Sofia , Ginaldi Fabrizio , Raparelli Elisabetta , Fila Gianni , Bregaglio Simone
Published: February 2023

Assimilating remote sensing data into crop models has unrivaled potential to enhance the accuracy of yield forecasting systems. The main benefit is the increased adherence of the simulations to observed vegetation dynamics related to farmers management and environmental drivers. Here we propose a new methodology to perform sub-regional yield forecasting, grounded on the derivation of leaf area index (LAI) from remote sensing as input for crop model calibration. The study area comprised 23 NUTS-3 administrative units located in three Italian regions, i.e., Apulia (south), Tuscany (centre) and Veneto (north). Crop maps of barley and maize in 2018 and 2019 were used to derive time series of Normalized Difference Vegetation Index (NDVI), which were tailored in the growing season according to agro-meteorological rules. Multivariate analyses of NDVI allowed identifying local agrotypes and associated LAI time series, which were used to develop parameter sets of the WOFOST_GT model via automatic calibration. Improvements in explaining the variability of official yields were evaluated in hindcast mode (2007-2017) using model parameterizations from literature as the baseline. Crop simulations reflected the latitudinal phenological gradient, leading to the anticipation of 15-30 days of barley and maize flowering and maturing dates from Veneto to Apulia. Simulated LAI dynamics reproduced observations with modelling efficiency always higher than 0.35 in calibration, and average Relative Root Mean Square Error (RRMSE) in explaining yield variability was in the range 15-17% for barley and 5-17% for maize in hindcast mode. The new procedure reduced by 51% and 14% the average RRMSE in yield prediction with respect to the baseline, decreasing by more than 1 Mg ha -1 the absolute errors on both crops. This methodology is scalable to other crops and regions, and the released parameter sets are ready to be used for operational yield forecasting activities.
Implementing geophysical and geochemical data in multi-criteria analysis for prioritization of munition dump site clearance
Authors: Ensenbach Samar , Pechmann Alexander , Frey Torsten
Published: February 2023

There is about 1,6 million tons of dumped ammunition lying in the German Baltic and North Sea waters, which is not only toxicologically threatening the aquatic ecosystems, but also hindering anthropogenic off-shore activities such as construction of wind parks, pipelines and fisheries. Although, the German federal government is committed to providing considerable funds, the large amount of munition in the German Seas and the complexity of retrieving it, make remediation efforts time consuming and costly. An important question is therefore, “How should dumped munition sites in the German Baltic Sea be prioritized for clearance?”. The focus on the Baltic sea results from the amount of available data.This poster will showcase how a priority list for the remediation of dump sites of conventional munition in the German Seas will be generated using multi-criteria analysis. This decision depends on a multitude of factors, ranging from the availability of information, geophysical features, collected biological, chemical and toxicological data, level of corrosion, proximity to beaches, severity of contamination, distance to infrastructure as well as the preferences of the stakeholders involved.To structure the multiple factors and to capture preferences of diverse stakeholders, the Analytic Hierarchy Process (AHP) will be used. AHP helps guide complex decisions regarding a set of alternatives for dealing with a particular problem. This is done by structuring relevant criteria within a hierarchy tree and comparing them pairwise through stakeholder participation. This procedure will aid the computation of a final priority list of which dumping sites to clear first. This result will reflect the priorities of the different groups of involved stakeholders, the multiple factors and it will indicate what kind of additional information may need to be generated for future analysis. The poster will therefore, exemplify how geophysical and geochemical data can be used to assist political decision making.
The HEliospheric pioNeer for sOlar and interplanetary threats defeNce (HENON) mission
Authors: Laurenza Monica , Marcucci Maria Federica , Zimbardo Gaetano , Landi Simone , Paglialunga Daniele , Di Tana Valerio , Provinciali Lorenzo , Cicalò Stefano , Vainio Rami , Lehti Jussi , Němeček Zdeněk , Prech Lubomir , Safrankova Jana , Eastwood Jonathan , Brown Patrick , Walker Roger , Jiggens Piers , Natalucci Silvia ,   HENON Team
Published: February 2023

The HEliospheric pioNeer for sOlar and interplanetary threats defeNce (HENON) is a new mission concept conceived to address the widely recognized need to make a leap forward in the Space Weather (SWE) forecasting and science. The HENON baseline foresees one 12U CubeSat orbiting along a Distant Retrograde Orbit (DRO) of the Sun-Earth system, so that the HENON CubeSat will stay for a long period of time very far upstream of the Earth (well beyond L1 at least ~ 0.1 AU). HENON will embark a state of the art radiation monitor, which will provide high-resolution measurements of energetic particle spectra, making HENON the first mission ever providing a real time monitoring of the particle radiation environment in the deep space. This will enable the insight into the near-Earth spatial variations of SEP events giving rise to better boundary conditions for forecasting and nowcasting tools. The HENON mission also aims to embark payloads tailored for SWE observations, in order to pave the way for a significant improvement (several hours) of the forecasting horizons of geo-effective interplanetary structures (ICMEs, HSSs). HENON has important technological objectives including demonstration of the capability of the CubeSat technologies in deep space to reach both scientific and operational goals through the first ever operation in unexplored DRO orbits, thus paving the way for a future fleet of such CubeSats equally spaced along the DRO, which could provide continuous near real-time measurements for space weather forecasting. HENON is in the A/B study phase that is being developed in the framework of the ESA General Support Technology Program (GSTP). HENON is funded by the Italian Space Agency as part of the ALCOR programme.
Demography DGVMs, Forest Management, Reforestation, and Afforestation: Evaluations of JULES-RED at a Sitka Spruce Plantation
Authors: Argles Arthur , Robertson Eddy , Harper Anna , Morison James , Xenakis Georgios , Hastings Astley , Mccalmont Jon , Moore Jon , Bateman Ian , Gannon Kate , Betts Richard , Bathgate Stephen , Thomas Justin , Heard Matthew , Cox Peter
Published: February 2023

Afforestation and reforestation are necessary for many countries to meet their Nationally Determined Contributions (NDC) and Net Zero targets (Seddon, N. et al 2019). Many countries estimate carbon sequestered using simple land-based transitions (IPCC, 2006) or have more complicated empirical estimations of carbon sequestered through forestry (Thomson, A. et al. 2020). This often leads to differences between NDC inventory submissions and bio-geophysical land surface modelling (Grassi, G. et al 2022).   The increasing representation of plant/tree demography within the latest Dynamic Global Vegetation Models (DGVMs) (Argles, A. et al 2022) and the availability of higher resolution regional climate datasets, provides new scope to evaluate modelled estimates against national inventories and Net Zero policies. As much afforestation is likely to be in the form of managed forests (Bateman, I.J, et al. 2022), the impact of management needs to be represented within models. We evaluate the JULES-RED model (Argles, A. et al 2020) against measured C stocks and fluxes at Harwood Forest, a Sitka spruce plantation in the UK. Planted in 1973, we will show that the inclusion of thinning in JULES-RED contributes to a more realistic representation of the 2018 carbon stocks and tree size-structure. We estimate a non-linear initial age-effect followed by a linear divergence between fixed and varying CO2 simulations, highlighting the need for better understanding of the effect of CO2 fertilisation in even-age stands and plantations.
Sub-Alfvénic solar wind streams near the earth: characteristics and their origin
Authors: Lin Rong , He Jiansen , Hou Chuanpeng
Published: February 2023

The PSP observation of a long-lived sub-Alfvénic solar wind, along with its magnetic-dominant character, marks a milestone that a human spacecraft has entered the solar corona for the first time (Kasper et al. 2021 PRL). In fact, sub-Alfvénic solar wind streams have also been observed multiple times near the earth by WIND spacecraft. What are the difference and possible connections between the sub-Alfvénic streams very close to the sun and the sub-Alfvénic streams 1 au from the sun? What process generates and sustains the near-earth sub-Alfvénic streams as they propagate outwards? Why the yearly occurrence frequency of them strongly correlates with solar activity? We study several sub-Alfvénic streams, which can be categorized into two groups: sub-Alfvénic background solar wind and sub-Alfvénic ICMEs. In-situ observations, remote observations, and connecting tools are used in our study. We find the sub-Alfvénic background streams are magnetic enhancements embedded in rarefactions. Their origin can be the boundary of the expanding coronal holes and shrinking active regions. A sub-Alfvénic ICME is generally a low-density part of the whole ICME, whose solar origin tends to be elusive in the coronagraph but still geomagnetically effective because of the ICME magnetic field.
Closing the loop between integrated assessment and climate risk research – rapid climate risk emulation
Authors: Byers Edward , Werning Michaela , Krey Volker , Riahi Keywan
Published: February 2023

Climate model emulation has long been and is increasingly applied to the results of integrated assessment modelling (IAM)models (IAMs), to determine the climate outcomes, primarily global mean surface temperature (GMST), of emissions pathways. Originally provided at the global level, more recently approaches have been developed to reproduce a growing number of climate variables, also with spatial, even gridded, resolution. Here we build on these approaches to demonstrate a workflow and post-processing package, that takes the GMST trajectory, e.g. from a simple climate model (SCM) and calculates a range of climate impacts and exposure indicators based on the GMST trajectory. To do this, we built a database of post-processed climate impacts from global climate [WM1] CMIP6 & ISIMIP-3 GCMs and impacts [WM2] models, and also calculated population and land area exposure to the indicators through time and for spatial units, e.g. countries. Indicators include temperature and precipitation extremes, heatwaves, degree days, drought intensity, water stress, and indicators of hydrological variability. Using a high-resolution temperature time timeslice approach, GMST trajectories are then mapped to the impact and exposure indicators to produce gridded maps of climate impacts through time, and trajectories of climate exposure by spatial unit. Using this approach we demonstrate the rapid post-processing of SCM [WM3] results such that ensembles of global IAM [WM4] mitigation pathways, such as those from AR6, can be accompanied by a new suite of climate impacts and risk information – and discuss related uncertainties and avenues for further research to incorporate vulnerabilities.
Chemical stabilization of heavily contaminated mine soils using technosols made from non-hazardous industrial wastes
Authors: Fernández Landero Sandra , Fernández Caliani Juan Carlos , Giráldez Díaz Inmaculada , Morales Carrillo de Albornoz Emilio , Ruiz Montoya Mercedes , Barba Brioso Cinta , González Díez Isabel
Published: February 2023

Abandoned mine lands often have lost their natural resilience and adaptive capacity to retain potentially toxic trace elements (PTE) and, therefore, can act a secondary source areas of acidic effluents and harmful contaminants into surface and pore waters. To address this issue, a batch leaching test was conducted to assess the effects of tailored combinations of non-hazardous industrial wastes on the immobilization of PTE in three mine soils (MS2, MS3, MS4) of the historic Rio Tinto mining district (Spain). To do this, different Technosols (T) were prepared by mixing, at a ratio of 60:40 (w/w), organic (R2: sludge from the clear treated water) and inorganic waste materials with acid-neutralizing capability (R4 and R5: slags from the iron and steel making industry) and adsorbent properties (R6: red gypsum). The Technosols tested were made of: (T0) exclusively MS2, MS3 and MS4 (controls); (T1) 80%MS4+20%[R2+(R5+R6)]; (T2) 75%MS3+25%[R2+(R5+R6)]; (T3) 75%MS2+25%[R2+R4]; (T4) 75%MS2+25%[R2+R5]; (T5) 75%MS3+25%[R2+R5]; and (T6) 75%MS4+25%[R2+R5]. The leaching test was performed according to the standard EN-12457-4, at a liquid-to-solid ratio of 10 L kg-1 with constant agitation for 24 h. The leaching conditions were recorded in terms of pH and Eh values, and the PTE concentrations in the leachate solutions were analyzed by ICP-MS. Results showed that the leachates from untreated MS were ultra acid (pH 2.5-3.5) and had concentrations of Cu, Pb and Zn as high as 556 mg kg-1 (MS3), 90 mg kg-1 (MS4) and 28 mg kg-1 (MS3), respectively, while the released concentrations of Cr, Ni, As and Cd were below 1 mg kg-1. In relative terms, the most mobile PTE were Cd, Cu and Zn, which accounted for 12.6%, 10.3% and 5.4% of its total concentration, respectively. The Technosol application was able to buffer the soil pH to average values of 7.6, thus enhancing the attenuation of PTE by chemical fixation. The mobile fraction of PTE was drastically reduced or even rendered virtually negligible (<0.10%) except for Ni, which was leached from the T1 up to 0.79% of its total concentration, and for Cd (0.49% from the T2). In particular, T3 was the most effective for reducing the mobility of Cd, and T4 and T5 showed promise for assisting in the attenuation of Cu. Chemical speciation calculations predict that most of the PTE leached from untreated MS are in the form of sulfate complexes, comprising 62-77% of the dissolved fraction, and to a lesser extent as free ions. Upon treatment, the activity of such species decreased noticeably with increasing pH, while the contribution of hydroxyl complexes was shown to increase. In conclusion, the use of Technosols was effective in reducing the acid generation potential of the MS and for boosting the PTE stabilization processes.
A hybrid data assimilation and machine learning approach for improving forecast skill in models strongly driven by boundary conditions
Authors: Cremer Clemens , Mariegaard Jesper , Andersson Henrik
Published: February 2023

Data assimilation (DA) has shown to be a powerful method to improve hydronumeric models by integration of observations. However, in systems which are strongly governed by boundary conditions, the benefits of DA are typically limited to improvements of hindcast and quickly fade in forecasts where no observations are available. Where sufficient historical observations are available, machine learning (ML) models can be an attractive alternative for providing accurate forecasts of hydrodynamic conditions.With the goal to improve the forecast, we propose a hybrid approach combining a physics-based numerical model with a machine learning approach via data assimilation. The ML model delivers accurate forecasts in a few points where historical observations are available. These results are then treated as synthetic observations by data assimilation which transfers the improvements to surrounding positions and other model variables.This approach is illustrated with a case-study from the Elbe Estuary. Here, an operational two-dimensional hydronumeric model, strongly driven by boundary conditions, is used to compute the hydraulic conditions in the estuary with the main purpose of ensuring safe nautical navigation. The existing model is extended with an Ensemble Kalman Filter data assimilation approach where an ensemble is created by stochastic perturbation of model forcings.During hindcast, data from two stations of the estuary are assimilated to improve numerical model results and initial conditions for the forecast. To maximize forecasting skill, a Long-short-term-memory machine learning approach is used to provide synthetic observations at assimilation stations during the forecast.Results from the hybrid model compared to a baseline model, without assimilation, at independent validation stations show that the hybrid model can reduce forecast errors by 40% for water levels and prolongs the positive influence of data assimilation significantly.
Bias correction of climate models using observations over Antarctica.
Authors: Carter Jeremy , Chacón Montalván Erick , Leeson Amber
Published: February 2023

Regional Climate Models (RCM) are the primary source of climate data available for impact studies over Antarctica. These climate-models experience significant, large-scale biases over Antarctica for variables such as snowfall, surface temperature and melt. Correcting for these biases is desirable for impact models being driven by meteorological data that aim to produce optimal estimates of for example surface run-off and ice discharge. Typical approaches to bias correction often neglect the handling of uncertainties in parameter estimates and don’t account for the different supports of climate-model and observed data. Here a fully Bayesian approach using latent Gaussian processes is proposed for bias correction, where parameter uncertainties are propagated through the model. Advantages of this approach are demonstrated by bias-correcting RCM output for near-surface air temperature over Antarctica.