bluuebunny/crossref_metadata_2025 huggingface dataset:
Cooling rates and thermal-geomechanical properties of the lava flows of Tajogaite eruption (La Palma, Canary Islands) for land use planning and infrastructures reconstruction.
Authors: González de Vallejo Luis , Miranda-Hardisson Ana , Álvarez-Hernández Aarón , Rodríguez-Losada José A. , Hernández Pedro A. , Hernández-Gutiérrez Luis E. , Cervigón-Tomico Germán , Villepreux Ernaud , de los Ríos Díaz Héctor , Afonso-Falcón David , Álvarez Díaz Antonio J. , Pérez Nemesio M.
Published: February 2023

Lava flows emitted by the Tajogaite eruption  (La Palma, Canary Islands) have covered an approximate area of 12 million m2, with thicknesses of up to 80 m. Type Aa flows represent more than 90% of the surface, the rest being Pahoe-hoe type. The urgent need to rebuild the affected roads has been the highest priority, along with territorial planning with urban proposes, infrastructures and agricultural development, being the lava temperature one of the most critical restrictive condition.Geological, geotechnical and thermal investigations have been carried on including 4 boreholes up to 10 m deep in which a detailed geotechnical characterization and systematic measurement of temperatures at different depths. Temperatures measurements on the lava surface were also recorded using drones and in situ thermocouples. Detailed maps of lava flow thicknesses and geological cartographies, including fractures and volcanic cavities have been made. An empirical model of lava cooling rates has been developed based on geological, geotechnical data and temperature measurements.Additionally a new cooling rate model has been developed as a function of depth, based on exponential equations in accordance with Newton's cooling law that relate temperature to time and whose main parameter is the cooling coefficient. This model has been applied to different lava depths. The results obtained indicate an acceptable agreement between the numerical models and the recorded data of temperatures. These results have been applied to the reconstruction of infrastructures and urban planning, allowing to identify favorable areas for infrastructures reconstruction and land use planning.
Inclusion of additional data streams within atmospheric inverse modelling systems: first results from ITMS
Authors: Gerbig Christoph , Koch Frank-Thomas , Munassar Saqr , Custodio Danilo , Galkowski Michal , Rödenbeck Christian
Published: February 2023

Within the Integrated Greenhouse Gas Monitoring System for Germany (ITMS), a national initiative targeted at the provision of estimates of GHG fluxes for Germany, the CarboScope-Regional (CSR) inversion system operated at the MPI-BGC is envisioned as a back-bone and reference system for future developments using the ICON-ART modelling system at the German Weather Service (DWD). It utilizes ICOS atmospheric observationsAdditional focus of the research involving CSR is the evaluation and improvement of vertical transport in atmospheric models, as well as the inclusion of additional data streams (e.g. vertical profile information) to improve both, GHG flux estimates and their uncertainty characterization. In this context, the assessment of mixing heights as represented within CSR against independent information derived from radiosondes over a timespan of more than a decade has revealed problems vertical mixing during wintertime that have the potential to cause biased flux retrievals. First results related to this and other experiments will be presented.
Comparison study on the deep-learning-based detection of Mars craters
Authors: AlRiyami Hind , Gebhardt Claus , Lee Christopher
Published: February 2023

Deep-learning methods are of interest for the analysis of imagery and digital elevation models from Mars orbiting satellites. They detect various atmosphere and surface characteristics. For instance, these include dust storms and craters [1,2]. We approach this topic by using the deep-learning-based crater detection algorithm DeepMars2 [3,4]. The algorithm is applied to two digital elevation models (DEMs) of the Mars surface. The DEMs are based on the satellite instruments MOLA/MGS (Mars Orbiter Laser Altimeter/Mars Global Surveyor) and HRSC/MEX (High Resolution Stereo Camera/Mars Express) and have different resolution. Crater detection statistics are compared between both DEMs.[1] Alshehhi, R., Gebhardt, C. Detection of Martian dust storms using mask regional convolutional neural networks. Prog Earth Planet Sci 9, 4 (2022). https://doi.org/10.1186/s40645-021-00464-1[2] R. Alshehhi and C. Gebhardt, "Automated Geological Landmarks Detection on Mars Using Deep Domain Adaptation From Lunar High-Resolution Satellite Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 2274-2283, 2022, doi: 10.1109/JSTARS.2022.3156371.[3] Lee, C. (2019). Automated crater detection on Mars using deep learning. Planetary and Space Science, 170, 16-28. https://doi.org/10.1016/j.pss.2019.03.008[4] Lee, C. & Hogan, J. (2021). Automated crater detection with human level performance. Computers & Geosciences, 147, 104645. https://doi.org/10.1016/j.cageo.2020.104645
Regional Impact of Snow-Darkening During a Severe Saharan Dust Deposition Event in 2018 Across Eurasia
Authors: Rohde Anika , Vogel Heike , Hoshyaripour Gholam Ali , Kottmeier Christoph , Vogel Bernhard
Published: February 2023

Aerosols such as mineral dust particles reduce the surface albedo when deposited on snow. This leads to increased absorption of solar radiation. Especially in spring, this phenomenon can lead to increased snowmelt, which triggers further feedbacks at the land surface and in the atmosphere. Quantifying the magnitude of dust-induced variations is difficult because of the high variability in the spatial distribution of mineral dust and snow. We present an extension of a fully coupled atmospheric and land surface model system to investigate the effects of mineral dust on snow albedo across Eurasia. In a comprehensive ensemble simulation study, we investigated the short-term effects of an extreme Saharan dust deposition event in 2018. We found region-dependent feedbacks. Mountainous regions and areas near the snowline showed a strong impact from mineral dust deposition. The former showed a particularly strong decrease in snow depth. For instance, in the Caucasus Mountains we found a mean significant decrease in snow depth of -1.4 cm after one week. The latter showed a stronger feedback effect on surface temperature. In the flat region around the snow line, we found a mean significant surface warming of 0.9 K after one week. This study shows that the effects of mineral dust deposition depend on several factors. Primarily, these are elevation, slope, snow depth, and fraction of snow cover. Therefore, especially in complex terrain, it is necessary to use fully coupled models to study the effects of mineral dust on the snowpack and the atmosphere.
Deep learning approach for real-time tsunami impact forecasting in near field context – application to the French Mediterranean coastline
Authors: Andraud Pierre , Gailler Audrey , Dias Frédéric , Vayatis Nicolas
Published: February 2023

Tsunami warning systems currently focus on the first parameters of the earthquake, based on a 24-hour monitoring of earthquakes, seismic data processing (Magnitude, location), and tsunami risk modelling at basin scale.The French Tsunami Warning Center (CENALT) runs actually two tsunami modelling tools where the water height at the coast is not calculated (i.e., Cassiopee based on a pre-computed database, and Calypso based on real time simulations at basin scale). A complete calculation up to the coastal impact all along the French Mediterranean or coastline is incompatible with real time near field or regional forecast, as nonlinear models require fine topo-bathymetric data nearshore and indeed a considerable computation time (> 45 min). Predicting coastal flooding in real time is then a major challenge in such context. To overcome these limitations, non conventional approches such as machine learning methods are being explored. Among the huge number of actual models, deep learning techniques are becoming increasingly popular. Severals studies have shown the interest of using MLPs (Multilayer perceptrons) and CNNs (Convolutional neural networks) to quickly transform a deep ocean simulation result into a coastal flooding model. Once trained on a specific output area with a large dataset, the networks are able to predict in seconds the tsunami inundation map from any earthquake scenario drawn from a seismic source database representative of the seismotectonic context of the region of interest.A first study training neural networks to predict the maximum water height maps was performed on three specific French cities (Nice, Antibes and Cannes) to evaluate the capacity of the models to reproduce the ground truth. The objective here is to extend the method to predict, in addition to maximum wave heights and runups, maximum retreats and currents along the entire French Mediterranean coastline. The spatial resolution of the finer bathymetric grids is set to 25 meters. To be representative of reality, the training dataset is fed with seismic scenarios derived from the CENALT fault database and taking into account a stochastic slip distribution. The method provides promising early results.
Short term forecast of CME flux rope signatures using 3DCORE
Authors: Amerstorfer Ute , Rüdisser Hannah , Weiss Andreas , Möstl Christian , Amerstorfer Tanja , Bauer Maike
Published: February 2023

The 3D coronal rope ejection (3DCORE) model has proven to work quite well for fitting in situ magnetic fields of CME flux ropes. The model assumes an empirically motivated torus-like flux rope structure that expands self-similarly within the heliosphere, is influenced by a simplified interaction with the solar wind environment, and carries along an embedded analytical magnetic field. For the fitting part an approximate Bayesian computation sequential Monte Carlo algorithm is utilized, which allows us to generate estimates on the uncertainty of model parameters using only a single in situ observation.In the present study, we test the ability of 3DCORE to perform short term forecasts of an ICME’s magnetic field. Therefore, we use only the first couple of hours of an in situ observation to which 3DCORE fits a magnetic field and predicts the rest of the flux rope structure.
Societal Flood Risk in Italy
Authors: Yazdani Mina , Salvati Paola , Rossi Mauro , Bianchi Cinzia , Guzzetti Fausto
Published: February 2023

Flood events are among the most damaging natural disasters, with billions of people being directly exposed to the risk of intense flooding worldwide. The economic and societal consequences of these events are expected to increase in the coming years. Flood societal risk can be determined by analyzing the relationship between the frequency of fatal flood events and the magnitude of the resulting consequences to the population (evaluated by the number of fatalities due to the event). Here, we test an approach previously proposed for landslides to estimate the flood societal risk in Italy, using historical sparse data on flood fatalities, available through national catalogues. Such an approach is based on the use of the Zipf distribution, which has previously been widely adopted for the modeling of societal risk for different natural hazards. The model allowed the evaluation of the spatial and temporal distribution of societal flood risk over the Italian territory over a regularly spaced grid. Different risk scenarios are presented and discussed.  
Exploring the effects of terrestrial exoplanet bulk composition on long-term planetary evolution
Authors: Spaargaren Rob , Ballmer Maxim , Mojzsis Stephen , Tackley Paul
Published: February 2023

New terrestrial exoplanets are being discovered at an ever faster pace, and each discovery leads to a widening of our understanding of planetary diversity. A key aspect in the quest to better quantify terrestrial planet diversity is to gain information on plausible bulk compositions, as this physical-chemical quantity determines the planet's structure, which in turn controls physical properties of the its layers (core, mantle, crust, atmosphere). Recent insights in the expected range of bulk planet compositions allow us to investigate how this fundamental parameter affects the evolution of the planetary interior and surface, and consequently to guide next-generation ground- and space-based telescopic observations of exoplanet properties, such as atmospheric composition.Here, we first simulate mantle mineralogies for exoplanets with various bulk compositions, using a Gibbs energy minimization algorithm, Perple_X. Using mineralogy and resulting physical properties, we employ a 2D global-scale model of thermochemical mantle convection to investigate the variations between Earth-sized exoplanets of different compositions in terms of interior evolution. We include the effects of composition on planet structure, mantle physical properties, and mantle melting. We investigate how composition affects thermal evolution, and whether it has an effect on the propensity of a planet towards plate tectonics-like behaviour.In general, Earth tends to have an average composition for most elements, except for iron, which it is relatively rich in, and therefore it has an above average core size. Our preliminary results show that core size (and thus iron abundance) affects convective vigor, and thus thermal evolution of the interior. We further find major differences for planets with different ratios of Mg-silicates, as these minerals control mantle viscosity, and thereby thermal evolution. Planets with lower Mg/Si than Earth will have a significantly stronger mantle, impeding cooling on planetary lifetimes, while planets with much higher Mg/Si have weaker upper mantles, impacting surface mobility. Stellar Mg/Si is a good indicator of the relative abundances of these minerals, and can be an important source of information. Therefore, the host stellar abundances seem to be an indicator of rocky planet properties, and can be used in the target selection for future missions.
Prediction of Surface Soil Moisture Content using Multispectral Remote Sensing and Machine Learning
Authors: Khose Suyog , Mailapalli Damodhara Rao
Published: February 2023

Information on near-surface soil moisture content (SMC) is very important for various applications such as irrigation scheduling, precision farming, watershed management, climate change analysis, drought prediction, meteorological investigations etc. Soil moisture information acquired from remotely sensed satellite data has been widely used in the recent past. However, these remote sensing data's low spatial and temporal resolution is a limitation for agricultural applications. Unmanned aerial vehicles (UAV)-based soil moisture predictions are thriving, but the studies are limited with fewer ground truth data. This study aims to predict the surface soil moisture content using UAV-based multispectral data and machine learning techniques. The UAV-based multispectral data are acquired from an altitude of 40 m. Surface soil samples were collected at an interval of two days to estimate gravimetric soil moisture content. Four machine-learning algorithms (Linear Regression, SVR, RFR, KNN) were used to develop the relationship between near-surface SMC and multispectral data. At high surface SMC, the soil has low spectral reflectance as compared to low surface SMC. The linear regression algorithm performed best, with R2 as 0.89 among the other ML algorithms. Also, blue band reflectance was correlated well with the surface SMC as compared to green, red, NIR and red-edge bands. The findings indicated that UAV-based high-resolution multispectral image analytics could accurately predict the surface SMC. The developed approach of estimation of near SMC may be helpful for farmers and irrigation planners to schedule irrigation and crop management accordingly.Keywords:  Surface soil moisture content; Remote sensing; UAV; Multispectral imageries; Machine learning
Estimates of soil heterotrophic respiration in global terrestrial ecosystems
Authors: Tang Xiaolu , Yang Zhihan , Zhou Tao
Published: February 2023

Soil heterotrophic respiration (RH) is one of the largest and most uncertain components of terrestrial carbon cycle, which reflects the carbon loss from soils to the atmosphere due to microbial decomposition of soil organic carbon and litter debris. However, RH estimates vary greatly using different approaches, requiring RH estimates from different angles. Therefore, in current study, we first modelled RH from 1980 to 2016 using a Random Forest (RF) algorithm with the linkage of field observations from the Global Soil Respiration Database and global environmental drivers at 0.5 degree; second, we analyzed the spati-temporal patterns of RH; and third, we compared RF-driven RH with estimates from Global Dynamic Vegetation Model (GDVM) and Data-Model Integration approaches. Results showed that RF could satisfactorily capture the spati-temporal patterns of RH with a model efficiency of 50% and root mean square error of 143 g C m-2 a-1. RF-driven RH showed a large spatial variability and decreased with the increasing latitude. Total RF-driven RH was 57 Pg C a-1 (1 Pg = 1015 g) with an average increasing trend of 0.036 Pg C a-2 from 1980 to 2016 (p < 0.001). However, the temporal trend of RH varied with climatic zones that RH increased in boreal and temperate areas, while no temporal trend in tropical regions. RH from seven GDVMs changed from 34.8 Pg C a-1 for ISAM model to 59.9 Pg C a-1 with an average of 47.6 Pg C a-1, underestimating RH by 9.6 Pg C a-1 (16%) in comparison to RF-driven RH. Furthermore, RH estimates from data-driven approach of Hashimoto et al. (2015) and Yao et al. (2021) were 51 and 47 Pg C a-1, which were lower than our estimate. Such difference was mainly attributed to different modelling algorithms and observational datasets. Therefore, given the potential uncertainties remaining in RH products, new approaches, e.g. deep learning or better representation of soil carbon cycling processes in GDVMs, are encouraged.  *This study was supported by the National Natural Science Foundation of China (32271856). 
Escape by jumps and diffusion by 𝛼-stable noise across the barrier in a double well potential
Authors: del Amo Ignacio , Ditlevsen Peter
Published: February 2023

Inspired by the previous evidence that the DO events can be modelled as transitions driven by Lévy noise, we perform a detailed numerical study of the average transition rate in a double well potential for a Langevin equation driven by Lévy noise. The potential considered has the height and width of the potential barrier as free parameters, which allows to study their influence separately. The results show that there are two different behaviours depending on the noise intensity. For high noise intensity the transitions are dominated by gaussian diffusion and follow Kramer’s law. When noise intensity decreases the average transition time changes to the expected power law only dependent on the width on the potential and not on the height. Moreover, we find a scaling under which the transition time collapses for all heights and widths into a universal curve, only dependent on 𝛼.
Two centuries of shoreline change in Northern Ireland.
Authors: Grottoli Edoardo , Biausque Melanie , Jackson Derek W. T. , Cooper J. Andrew G.
Published: February 2023

Shoreline change analysis of the entire coast of Northern Ireland was conducted for the period from 1830 to 2021. Even with its 790Km length (including the main offshore islands) the Northern Ireland coastline is remarkably diverse, comprising several notable coastal typologies (cliffs, rocky coasts, sandy beaches, sand dunes, etc.) as well as a number of commercial and industrial activities. Human pressure co-exists with the natural coast and each impacts the other in many ways.Shorelines were digitised using multiple temporal and spatial datasets (historical maps, aerial photos and orthophotos). The average temporal interval of shorelines ranges from 7.8 to 37 years, depending on location and data availability. The seaward vegetation line was selected as the primary shoreline proxy, whereas cliff edge, rock-water line or anthropogenic structures were chosen subordinately or if deemed more appropriate. Shoreline uncertainty was assessed using various errors inherent from each dataset from which the shoreline was digitised. The ArcMap® tool DSAS 5.0 was used to calculate shoreline rates and distances along 25 m spaced transects covering the entire coastline.Over the last two centuries the maximum retreat value was highlighted in Lough Foyle between the Roe River mouth and Magilligan Point where the shoreline progressively retreated up to -250 m. The largest shoreline advancement (+3.500 m) was recorded in Belfast following its port expansion.Sandy coastlines were shown to have the larger natural changes. Magilligan point, after an advancement phase toward its northern extremity between 1830 and 1919, underwent a constant retreat phase that has now extended to its eastern side up. Benone Beach, Castlerock and Portstewart Strand are the only vegetated coastal dune sites along Northern Ireland’s north coast that are advancing in the last 190 years(190-270m) Significant shoreline retreat was recorded at White Rocks (-85 m), Ballycastle (-65 m), Runkerry (-25 m) and White Park Bay (-65 m). In Dundrum Bay, Co. Down, the largest retreat values (-75 m) occurred at Murlough and maximum advancements greater than 200 m at Ballykinler.The largest shoreline advancements were all attributed to human activities such as land reclamation in the loughs (mainly during the 19th century), seaward expansions of ports (e.g., Belfast, Bangor, Carrickfergus, Portavogie and Warren Point) or construction of power stations and wastewater treatment areas. Shoreline advancements recorded for the salt marshes of Larne and Foyle Loughs and White Water River mouth also followed human interventions. High rocky coasts, apart from limited rockfalls, were less subject to shoreline changes. The work will contribute to better define coastal cells along the Northern Ireland coastline and inform coastal managers for future development plans of the coast.
Imaging Upper Mantle Anisotropic Structure Using Teleseismic Shear Wave Delays and Splitting Intensity: Application to the Cascadia Subduction Zone
Authors: VanderBeek Brandon , Lo Bue Rosalia , Faccenda Manuele
Published: February 2023

Despite the well known anisotropic structure of Earth’s upper mantle, the effect of seismic anisotropy on the construction of body wave shear velocity models remains largely ignored. Ignoring anisotropic heterogeneity can introduce significant model artefacts that may be misinterpreted as compositional and thermal heterogeneities. While effective anisotropic imaging strategies that improve model reconstruction have been developed for P-wave delay times, no such general framework exists for S-waves partly because, unlike P-waves, there is not a simple ray-based methodology for predicting S-wave travel-times through anisotropic media. Here, we apply a new methodology for the inversion of relative shear wave delay times and splitting intensity measurements for arbitrarily oriented hexagonally anisotropic model parameters using data collected across the western United States and Cascadia subduction system. We detail the data analysis procedure required for making measurements of shear wave observables suitable for anisotropic inversions (e.g. determination of incoming polarisation directions). We then present a preliminary anisotropic shear wave velocity model for Cascadia and compare the results to purely isotropic images. The imaged anisotropic heterogeneity is compared to the well-established patterns in shear wave splitting parameters observed in the study area.
Testing sedimentary basin models for ground motion simulation: the case of the Fucino intramountain basin in the Apennines (Italy)
Authors: Sgattoni Giulia , Molinari Irene , Di Giulio Giuseppe
Published: February 2023

Sedimentary basins are of great interest for ground motion simulations, because of their power to amplify seismic motion and because urban areas are often built on sediment covers. Realistic and detailed 3D basin models have shown to significantly improve the physics-based ground motion modeling in terms of fit between recorded and synthetic seismograms. However, discerning between the uncertainties due to source, path or site effects is not simple.A good proxy of the seismic response of small- to moderate-scale sedimentary basins is their resonance frequencies, often investigated by experimental measurement of the Horizontal to Vertical spectral ratio (H/V) computed on ambient seismic vibrations or earthquake records. Since these parameters strongly depend on the geometry and mechanical properties of the sediment fill, a wavefield numerical simulation in a realistic 3D media should ideally reproduce them. The comparison of resonance frequencies obtained from real and simulated waveforms can help in discerning inconsistencies in the 3D models, and may help in evaluating the goodness of the model and highlighting areas where it may be improvedWe apply this approach in the Fucino intermountain sedimentary basin (Central Apennines, Italy) for which several stratigraphic models, exploration and geophysical data are available in the literature. We critically combine the stratigraphic models of the basin with regional crustal models available in the literature and build an appropriate 3D velocity model. We then perform 3D seismic wave propagation simulations using a spectral-element code; and we compare simulated and experimental seismograms and resonance frequencies for different basin models observing similarities and discrepancies.
Windows of opportunity for predicting seasonal climate extremes: Pakistan floods of 2022
Authors: Dunstone Nick , Smith Doug , Hardiman Steven , Ineson Sarah , Jain Shipra , Martin Gill , Scaife Adam
Published: February 2023

Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was widely predicted by seasonal forecasts. We argue that in this case, a strong summer La Niña provided a window of opportunity to issue a much more confident forecast for extreme rainfall than average skill estimates would suggest. We explore ways of building forecast confidence via physical understanding of dynamical mechanisms, perturbation experiments to isolate drivers, and simple empirical relationships. We highlight the need for more detailed routine monitoring of forecasts, with improved tools, to identify regional climate extremes and hence exploit windows of opportunity to issue trustworthy and actionable early warnings.
What motivates model developments? A multi-perspective case study from snow physics models.
Authors: Ménard Cécile , Rasmus Sirpa , Merkouriadi Ioanna
Published: February 2023

Historically, snow physics models were developed to forecast avalanches. Over the years, their application has broadened to hydrological, climatological, ecological and permafrost studies to cite but a few. However, the structure of mid-latitude mountain snowpacks upon which snow physics models are based (generally deep snowpack with snow denser at the bottom than at the top because of compaction) differs considerably from the structure of high latitude snowpacks (generally shallow snowpack with dense wind-compacted snow at the top and large snow crystals at the bottom). This difference has been known for decades to be a potentially large source of uncertainty when simulating heat exchanges in the Arctic and Antarctic. Therefore, with Arctic warming having consequences on the global climate, why have snow physics modellers not developed a model with a high latitude or “arctic snowpack” yet? Taking this question as a case-study to understand the role that subjective decisions play at every phase of model developments, we interviewed more than twenty snow physics model users (e.g. ecologists, anthropologists, remote sensing and climate scientists) and developers to understand the following: what motivates model developments? What or who determines which parametrization, which process is to be prioritised over others? What role does the research question play? What about funding or staff availability? We will show that positionality, anchoring bias and interpersonal relationships play far more prominent roles in the physical sciences that commonly acknowledged and will draw lessons from the social sciences to increase transparency in our modelling practice.
Hybrid dynamical/statistical forecasts of wet season onset using CCA
Authors: Colman Andrew
Published: February 2023

The Climate Predictability Tool (CPT) is a well established tool for creating calibrated objective predictions of seasonal rainfall anomalies, and is used for this purpose by many institutions including  the IGAD Climate Prediction and Applications Centre (ICPAC)  to create operational forecasts for the Greater Horn of Africa wet seasons. CPT can also be used to predict other variables such as wet season onset. Such predictions require a non spatially dependent definition of onset, in our case we define onset as the number of days into the season when rainfall reaches 10% of seasonal rainfall for that location. The CPT forecasts are created by detecting relationships between predictions of precipitation and SST from global dynamical forecasting systems and observed onset patterns using Canonical Correlation Analysis (CCA).  CPT has the advantage that skill statistics are automatically produced for assessing the performance of the forecasts. CPT forecasts of Short rains (October-December) onset have been found to have useful skill.
Juno Microwave Radiometer Observations of Europa’s Subsurface Ice Shell
Authors: Bolton Scott , Zhang Zimeng , Brown Shannon , Bonnefoy Lea , Leonard Erin , Levin Steve , Lunine Jonathan , Misra Sid , Hartogh Paul , Siegler Matt , Stevenson David , Trumbo Samantha
Published: February 2023

On 29 Sep 2022, Juno had a close flyby of Jupiter’s moon Europa, flying within 500 km of the surface. During the flyby, Juno’s Microwave Radiometer (MWR) observed Europa, obtaining several swaths across Europa using Juno’s spin to map Europa’s subsurface ice shell at six frequencies ranging from 600 MHz to 22 GHz.  The ice transparency at microwave frequencies is dependent on purity; assuming pure ice, the observations probe depths ranging from meters to kilometers. The MWR observations represent the first resolved interrogation of Europa’s subsurface ice shell revealing new constraints on porosity, fracturing, differences in terrain type and possibly the thickness of the conductive ice shell.  These unprecedented measurements on Europa and Ganymede will provide new insights into the  comparative nature of the surfaces and interiors of the Jovian satellites.
Seasonal to interannual variability of salinity on the Northeast U.S. continental shelf and links to large-scale North Atlantic variability
Authors: Ryan Svenja , Gawarkiewicz Glen G. , Ummenhofer Caroline C.
Published: February 2023

The Northeast U.S. continental shelf is a highly productive and economically important region that has experienced robust changes in upper-ocean properties in recent decades. Warming rates exceed the global and North Atlantic average and in particular several episodes of anomalously warm temperatures, so called marine heatwaves, have had devastating impacts on regional fisheries over the past decade. Stratification will strongly impact the magnitude of surface warming, i.e., a thin surface layer will heat faster, as well as facilitate cross-shelf exchange. Ocean salinity can play an important role in the development of seasonal stratification, ultimately also modulating horizontal density gradients. Here we use various available products of sea surface salinity to assess seasonal to interannual variability with focus on the continental shelf south of New England. Data from the Coastal Pioneer array from the Ocean Observatory Initiative provides an ideal basis for validation and furthermore provides valuable subsurface data that allows assessment of the relative roles of temperature and salinity in driving density variability and stratification. River discharge along the eastern U.S. and Canadian coast drives a seasonal freshening on the shelf, while shelf break exchange driven by the interaction with Gulf Stream warm core rings is associated with an onshore salt flux. Furthermore, large-scale forcing in the North Atlantic can impact the along-shelf advection of Arctic-sourced freshwater, which likely shifted the shelf to higher salinities (along with warmer temperatures) in the recent decade. Salinity serves as a valuable tracer, in addition to temperature (having a large diurnal and seasonal cycle), of these multi-variate processes. With now more than a decade of satellite surface salinity we aim to shed new light on the spatio-temporal variability across various scales on the Northeast U.S. continental shelf and how it is embedded in large-scale North Atlantic variability.
Impact of sliding laws and surface mass projections on Greenland outlet glacier dynamics at 100-year timescales
Authors: Carr Rachel , Hill Emily , Gudmundsson Hilmar
Published: February 2023

The Greenland Ice Sheet (GrIS) contributed to 10.6 mm to global sea level rise between 1992 and 2018, making it crucial to accurately forecast its near future ice losses. Here, we assess the relative importance of two major sources of uncertainty in GrIS ice loss, namely the choice of sliding law and SMB forecasts. To do this we use the ice flow model Úa to perform a series of model experiments using different formulations of the sliding law, and different projections of future surface mass balance (SMB). We conducted this work at three major Greenland outlet glaciers, to assess the variability in the importance of sliding laws and/or SMB forecasts between different types of glacier. Our results show that the choice of sliding law had a small impact on ice loss from our study glaciers, whereas the choice of SMB forecast produced major differences in sea level rise estimates.
Drivers and predictability of extreme summer Arctic sea ice reduction with rare event simulation methods
Authors: Sauer Jerome , Ragone Francesco , Massonnet François , Demaeyer Jonathan , Zappa Giuseppe
Published: February 2023

Various studies have identified possible drivers of extreme Arctic sea ice reduction, as observed in the summers of 2007 and 2012, including preconditioning, the oceanic heat transport and the synoptic-scale to large-scale atmospheric circulation. However, a quantitative statistical assessment of these drivers and a better understanding of the seasonal predictability of these events are hindered by the poor statistics of extremes in observations and in numerical simulations with computationally expensive climate models. Recent studies have addressed the problem of sampling extreme events in climate models by using rare event algorithms, computational techniques developed in statistical physics to increase the sampling efficiency of rare events in numerical models. In this work, we study the statistics of summer seasons with extremely low pan-Arctic sea ice area under pre-industrial greenhouse gas conditions, applying a rare event algorithm to the intermediate complexity coupled climate model PlaSim. Using the rare event algorithm, we oversample dynamical trajectories leading to events with extremely low summer and September mean pan-Arctic sea ice area. Compared to standard simulations of the same computational cost, we increase the sample size of the extremes by several orders of magnitude, which allows to perform statistically robust composite analyses of dynamical quantities conditional on these events. In addition, we have access to ultra-rare events with return times of up to 105 years. We exploit the improved statistics of summers with extremely low pan-Arctic sea ice area to study precursors of these events, including a surface energy budget analysis to disentangle the oceanic and atmospheric forcing on the sea ice. Particularly, we investigate the linkage between the extremes in summer Arctic sea ice area and the preceding states of the Arctic Oscillation and of the Arctic Dipole Anomaly pattern, as well as between the extremes and the preconditioning in the sea ice-ocean system during the onset of the melt season.
Evaluation of the carbonate system and air-sea CO2 fluxes in coastal waters of the Macaronesia archipelagos.
Authors: Curbelo-Hernández David , González-Dávila Melchor , Santana-Casiano J. Magdalena , González-González Aridane , González-Santana David
Published: February 2023

The CO2 system, anthropogenic carbon (Cant) inventory and air-sea CO2 fluxes (FCO2) were analysed in the archipelagic waters of the Macaronesia. The (sub)surface data were collected during POS533 (February and March, 2019) in coastal and open-ocean areas leeward of Cape Verde, Canary Islands and Madeira. The observed variability was controlled by changes in temperature, biological activity and advection processes forced by spatial heterogeneities in the Canary Upwelling System, the mixed layer depth, the mesoscale activity and the circulation patterns. The surface fCO2,sw variability was driven by biological production and CO2-rich water injection in tropical waters while temperature fluctuations controlled the fCO2,sw changes in subtropical waters. The factors controlling the upper ocean changes in the normalized total inorganic carbon (NCT) were assessed. The uptake and storage of anthropogenic carbon described > 60% (>90% above the MLD) of the NCT increase from preformed values. The organic carbon pump accounted for 36.6-40.9% in tropical waters and lose importance in subtropical waters (7.5-11.6%), while the carbonate pump has a minimal contribution (<4.2%). The upper-ocean Cant inventory in Cape Verde (8,570 Km2), Canary Islands (7.960 Km2) and Madeira (1,250 Km2) was 7.57 x 103, 9.26 x 103 and 8.86 x 103 µmol kg-1, respectively (0.51, 0.58 and 0.09 Tg C, respectively). In terms of FCO2, these archipelagos behaved as a winter CO2 sink (-4.74, -3.90 and -8.34 mmol m-2d-1, respectively) while a strong outgassing was detected over the Cape Blanc filament (20-25 mmol m-2d-1). The total average FCO2 for the ocean area of the three archipelagos (371,250 Km2) was -28.27 Gg CO2 d-1. The POS533 data were compared and combined with SOCAT and GLODAP data and a new set of equations was provided to calculate the fCO2,sw, Cant and FCO2 in the Macaronesian region based on physical and biogeochemical fluctuations.
Feedstock type and pyrolysis temperatures regulate the impact of biochar on soil health in arid land agroecosystems
Authors: Al Rabaiai Ahmed , Menezes-Blackburn Daniel , Al-Ismaily Said , Janke Rhonda , Pracejus Bernard , Al-Alawi Ahmed , Al-Kindi Mohamed , Bol Roland
Published: February 2023

AbstractBiochar amendments are gaining globally for improving soil health and carbon storage.  This study investigated the physicochemical properties and impact on soil microbes of biochar amendments derived from two feedstock sources: date palm leaves (D), and mesquite plants (M); pyrolyzed at 450, 600 and 750 ℃. SEM images revealed that the pore size increased with increasing pyrolysis temperature. According to the Fourier transform infrared spectroscopy results, the increase in pyrolysis temperatures reduced the O-H and C-O bonds while increasing the proportion of C-C bonds. The dynamic thermal gravimetric analysis evidence that the thermostability was greatest at a pyrolysis temperature of 750 °C, and was also significantly different for the two feedstock materials used. The M feedstock produced biochar with the highest surface area (600 m2 g-1) and carbon content based on loss on ignition (95%); moreover, this biochar reduced soil microbial enumeration and respiration, and this effect was more pronounced for biochar pyrolysed at 750 °C. As a result, M biochar feedstocks are not recommended for improving soil health, but they may be useful as microbial inhibitors when soil-borne plant pathogens are present. Based on the physicochemical properties and the biochar impact on soil properties, D at 600 °C was chosen as the best-performing biochar in our study for improving soil health in arid lands and was selected for further research as a soil amendment. The large differences in biochar physicochemical properties and their observed effect on soil properties confirmed that the feedstock type and pyrolysis temperatures must be considered during biochar production for soil health applications in arid-land agroecosystems.
Impact of weather and management practices on GHG dynamics on an agricultural grassland in southern Finland
Authors: Heimsch Laura , Vira Julius , Fer Istem , Vekuri Henriikka , Tuovinen Juha-Pekka , Nevalainen Olli , Kuvaja Karla , Lohila Annalea , Kulmala Liisa
Published: February 2023

Agricultural management has recently drawn substantial attention as regenerative practices are showing their potential in enhancing soil quality and fertility, biodiversity, and carbon uptake from the atmosphere into the soil. In Nordic countries, there is a great number of farmers farming regeneratively. However, they are still a minority, and we are lacking scientific studies on the effects of various improved practices and their combination and use in practice in northern growing conditions.We conducted a long-term study of an agricultural grassland ecosystem in southern Finland where regenerative management practices were implemented from 2018 onwards. We monitored H2O and CO2 fluxes with the eddy covariance method, and additionally, CH4, N2O and CO2 with flux chambers. We also measured meteorological variables and several soil and vegetation parameters. We studied the impacts of different cutting heights for the silage grass on the ecosystem carbon sequestration. Furthermore, we utilised the data in model simulations with the grassland model BASGRA to study the carbon dynamics of the agricultural ecosystem in various weather conditions and with different management decisions. Our results indicate that the studied years, which were differing greatly from the long-term average weather, may have had a great impact on carbon dynamics on the grassland. Both empirical data and modelling demonstrated net carbon accumulation into the ecosystem under the selected improved practices.
Sub-seasonal daily precipitation forecasting based on Long Short-Term Memory (LSTM) models
Authors: Bertini Claudia , Corzo Gerald , van Andel Schalk Jan , Solomatine Dimitri
Published: February 2023

Water managers need accurate rainfall forecasts for a wide spectrum of applications, ranging from water resources evaluation and allocation, to flood and drought predictions. In the past years, several frameworks based on Artificial Intelligence have been developed to improve the traditional Numerical Weather Prediction (NWP) forecasts, thanks to their ability of learning from past data, unravelling hidden relationships among variables and handle large amounts of inputs. Among these approaches, Long Short-Term Memory (LSTM) models emerged for their ability to predict sequence data, and have been successfully used for rainfall and flow forecasting, mainly with short lead-times. In this study, we explore three different multi-variate LSTM-based models, i.e. vanilla LSTM, stacked LSTM and bidirectional LSTM, to forecast daily precipitation for the upcoming 30 days in the area of Rhine Delta, the Netherlands. We use both local atmospheric and global climate variables from the ERA-5 reanalysis dataset to predict rainfall, and we introduce a fuzzy index for the models to account for seasonality effects. The framework is developed within the H2020 project CLImate INTelligence (CLINT), and its outcomes have the potential to improve forecasting precipitation deficit in the study area.
GNSS-derived Precipitable Water Vapor for Climate Monitoring
Authors: Dick Galina , Zus Florian , Wickert Jens , Männel Benjamin , Bradke Markus
Published: February 2023

Global Navigation Satellite System (GNSS) is now an established observing system for atmospheric water vapour with high spatiotemporal resolution. Water vapour is under-sampled in the current climate-observing systems and obtaining and exploiting more high-quality humidity observations is essential for climate monitoring. The Global Climate Observing System (GCOS), supported by the World Meteorological Organization (WMO), is establishing a reference climate observation network, the GCOS Reference Upper Air Network (GRUAN). Currently, this network comprises 30 reference sites worldwide, designed to detect long-term trends of key climate variables such as temperature and humidity in the upper atmosphere. GRUAN observations are required to be of reference quality, with known biases removed and with an associated uncertainty value, based on thorough characterization of all sources of measurement. In support of these goals, GNSS precipitable water (GNSS-PW) measurement has been included as a priority one measurement of the essential climate variable water vapor. The GNSS-PW program produces a nearly continuous reference measurement of PW and is therefore a substantial part of GRUAN. GFZ contributes to GRUAN with its expertise in processing of ground-based GNSS network data to generate precise PW products. GFZ hosts a central processing facility for the GNSS data and is responsible for the installation of GNSS hardware, data transfer, processing and archiving, as well as derivation of GNSS-PW products according to GRUAN requirements including PW uncertainty estimation. Currently half of the GRUAN sites are equipped with GNSS receivers. GNSS-PW products for GRUAN and the results of validation studies will be presented. 
Building on soil sustainability: Principles for soils in planning and construction
Authors: Davies Jess , Quinton John , O'Riordan Roisin , Hatch Paul , Dart Susanna , Birchall Adam , Hontzsch Birgit , Campion Charles , Farrer Noel
Published: February 2023

Soils are routinely undervalued, damaged and disposed of during construction and urban development. It is crucial that we not only stem the damage done to these vital ecosystems, but also actively consider how better planning and management of soil can result in environmentally and socially beneficial development. However, this is a significant policy and practice challenge. For example, currently in the United Kingdom, soils on construction sites fall within a gap in policy and legislation and government department remits. Large amounts of soil are being lost and damaged as a result. In 2018, 29.5 million tonnes of soil from construction sites were disposed of in landfill in the UK. Only 0.6 % of this was hazardous, which means a huge amount of this vital resource is being lost during construction. In this contribution, we will share evidence on the impacts of construction on soils and present a new set of guiding principles to help improve how soils and their multifunctionality are planned for and managed during construction and urban development. These principles were co-developed by a team spanning academia, local policymakers, master planners and landscape architects, and were formed in consultation with key policy and industry representatives. We will share insights generated through this process and reflect on what’s needed next for securing soil sustainability in planning and construction.