bluuebunny/crossref_metadata_2025 huggingface dataset:
Volcano engineering for food resiliency: uncertainty and ethics
Authors: Sandberg Anders
Published: March 2024

Given that global catastrophic events – or disasters at the wrong pinch point - can threaten the interconnected and fragile global food system, the obvious approach is to reduce system vulnerability. One could also consider reducing exposure: it might be possible to reduce the probability or severity of some of the geophysical risks. I will discuss the possibility of volcano engineering, the deliberate altering of volcanic systems, in this case for reducing global food risk.
Insofar this kind of reduction is possible, it raises both decision-theoretical and ethical questions. In terms of expected utility maximization reducing severe tail risks may sometimes dominate over reducing median risks, yet uncertainty (aleatory, model, and theoretical) will make any such dominance uncertain. Furthermore, such uncertainty will strongly affect the safety and rationality of the interventions themselves. Yet sufficient risk may still impel action. From an ethical standpoint, protecting large populations is a powerful reason for action but is similarly tempered by considerations of normative uncertainty, as well as justice, global governance, and our relationship with future generations.
Given these considerations I analyse two scenarios where volcano engineering might hypothetically be used at some point in the future, a normal volcano near a global pinch-point like the Straits of Malacca, and a hotspot volcano with high VEI threatening global agriculture, and analyse under what technological, decision-theoretical, and ethical conditions interventions would be proper.
Study of Martian Polar Caps with GISS ROCKE-3D GCM
Authors: Aleinov Igor , Glaser Donald , Guzewich Scott , Perlwitz Jan , Tsigaridis Kostas , Way Michael , Wolf Eric
Published: March 2024

Martian polar caps consist of both H2O and CO2 ice. While H2O ice is mainly passive on modern Mars, it may have not been the case in recent Martian history, when its obliquity was higher, or when it was changing rapidly. The distribution of ice species in the snowpack affects its physical and thermodynamic properties. In the upper layers, it determines its albedo and thermal emissivity. Thus understanding the mutual effect between these ices and their interaction with the atmosphere is crucial for understanding the evolution of Martian polar regions. In this study, we employ a newly-developed Exotic Ices snow model coupled to the NASA Goddard Institute for Space Studies (GISS) ROCKE-3D planetary General Circulation Model (GCM) [1]  to study the behavior of Martian polar caps. ROCKE-3D is a planetary GCM developed at NASA GISS as an extension of its Earth climate model, modelE [2]. It has been extensively used to simulate climate of various planets, including Mars (e.g. [3,4]).The Exotic Ices snow model was specially developed for planetary applications which involve more than one condensable in the atmosphere, in which case snow can contain multiple species of ice (CO2 and H2O in the Mars case). For each species of ice, the model uses their proper physical properties and phase diagram, but otherwise it treats all species of ice on an equal footing.  The combined effects on albedo, thermal inertia and mutual insulation are treated accordingly. The snowpack interacts with the atmospheric dust cycle, and can accumulate a prognostic amount of dust, though the effect of dust on snow properties is not currently treated explicitly, and is prescribed. In this study, we first validate our model against the modern Martial climate, for which we use mission results from Mars Climate Sounder (atmospheric temperature and dust optical depth), SPICAM on Mars Express (atmospheric water), and Viking 2 (surface pressure). We investigate the effect of snow radiative properties on CO2 and water cycles and the ability of our model to accurately reproduce those with minimal model tuning. We then perform simulations for several obliquities from a recent Martian past, and investigate the behavior of the Martian polar caps in such conditions.References: [1] Way, M. J. et al. (2017) ApJS, 231, 12. [2] Kelley, M. et al. (2020) J. Adv. Model. Earth Syst., 12, no. 8, e2019MS002025. [3] Schmidt, F. et al. (2022) Proc. Natl. Acad. Sci., 119, no. 4, e2112930118. [4] Guzewich, S.D. et al. (2021) J. Geophys. Res. Planets, 126, no. 7, e2021JE006825.
Event-based groundwater recharge: drip observations reach new depths in mine tunnels
Authors: Timms Wendy , Baker Andy , Shanafield Margaret , Andersen Martin , Priestley Stacey , Melo Zurita Marilu
Published: March 2024

Drip sensors in underground spaces such as mine tunnels can quantify vertical water flux through the vadose zone during rainfall events. Australia’s National Groundwater Recharge Observing System (NGROS), established in 2022, provides the first dedicated sensor network for observing the recharge of groundwater at an event-scale across a wide range of geologies, environments, and climate types. As part of this program, differences in recharge fluxes and patterns through fractured metamorphic rock were analysed for hourly data in a shallow mine tunnel (14 sensors at ~30-50 m below ground) and a deep mine tunnel (12 sensors at ~115 m below ground). Average annual rainfall varied from 1200 mm per annum at the deep and shallow tunnel sites respectively. Recharge was evident despite a thick (>100 m) unsaturated zone above the tunnel. In the shallow mine tunnel, distinct recharge pathways including fracture zones around quartzite intrusions were evident, and time-lags were relatively short. Data analysis will include patterns of rainfall thresholds at which vertical fluxes occur, and spatial patterns within tunnels and between different sites in the NGROS network. The drip data can be useful for managing mine water related risks, and to complement recharge estimates from other methods for sustainable groundwater management. 
Persistent winter merged jets over the Atlantic and extreme weather anomalies
Authors: Suresan Sohan , Harnik Nili , Caballero Rodrigo
Published: March 2024

Variabilities in the jet streams have a significant influence on our weather and climate, and could potentially increase the likelihood of a range of extreme weather events. The winter of 2009/2010 witnessed an unusual equatorward displacement of the Atlantic jet and its subsequent convergence with the African jet, leading to the emergence of a persistent zonally oriented merged jet. At the same time, intense and prolonged negative phase of the North Atlantic Oscillation (NAO) and unusually cold and extreme weather conditions were reported over the Northern Hemisphere. Such a merging was only observed to occur for a whole winter during the winters of 1968-69 and 1969-70. Preliminary results indicate that such persistent winter merged jets could be more frequent in a future global warming scenario and thus it is important to understand this dynamical regime transition of Atlantic jet and its effects on the weather patterns. In this study, we explore the extreme weather distribution over the northern hemisphere during such winter merged jets and its relation to NAO and ENSO. We show that merged jet winter months have a signature weather pattern distribution that is different from the negative NAO phase. We see a decrease in the surface eddy kinetic energy over the midlatitude during such winter leading to an equatorward shift in storm tracks over the Atlantic region and larger stormtrack density over the western Greenland which could potentially lead to the observed distribution of weather patterns. On comparing the surface temperature anomaly composites between the winters of strong negative NAO, EL Nino, and merged jet months we see that the merged jet winters have a significant persistent temperature distribution signature over the tropics and the Arctics. Similar analysis over the north hemisphere for surface wind, precipitation, and snowfall anomalies also shows a preferred persistent distribution over certain regions during the merged jet-state winters.
Prescribed burning as a mechanism to mitigate emissions of extreme fire events: a case study from the Brazilian Cerrado
Authors: Moura da Veiga Renata , von Randow Celso , Cardoso Manoel , Robertson Eddy , Burke Eleanor , Barbosa Maria Lucia , Burton Chantelle , Kelley Douglas , Morelli Fabiano
Published: March 2024

The risk of fire occurrence and the frequency of extreme fire events have been increasing globally due to climate change. As a result, greenhouse gas (GHG) emissions are higher worldwide, including in the Brazilian Cerrado. Cerrado is a fire-prone Biome in central Brazil, where fire is essential for maintaining the Biome diversity and integrity. Cerrado presents distinct rainy and dry seasons. In the dry season, the accumulated biomass available for burning becomes highly flammable and fire can rapidly spread from grasslands and savannas to forests. In fire-prone ecosystems globally, prescribed burning prevents intense and frequent wildfires in the drier months by intentionally applying fire under controlled conditions at the end of the rainy season and/or the beginning of the dry season. In Cerrado, prescribed burning is applied in the early dry season (EDS; April-June) to avoid severe wildfires in the late dry season (LDS; August-October), but so far there have been no documented estimates of the effect of prescribed burning on carbon emissions. In this study, we evaluate the potential of prescribed burning to mitigate emissions from extreme fire events in the Brazilian Cerrado region. We modelled fire emissions in Cerrado with JULES-INFERNO over a 30-year period (1990-2019), using the ISIMIP3a simulations. We adjust JULES-INFERNO to represent Cerrado, and then simulate prescribed burning by setting an additional ignition to C4 grass during EDS. We analyse years with large burned areas, including El Niño years, to represent years with intense fire events. Over the 30 years, prescribed burning resulted in reduced fire emissions in the LDS, especially in years when there was high burned area. This indicates that prescribed burning can be used in the Cerrado to reduce the impacts of uncontrolled fires in the drier months. We also observe that the effectiveness of prescribed burning in reducing emissions depends on the C4 grass recovery rate. Further investigation is needed to better understand the model’s performance, including analysis of modelled parameters such as the C4 grass post-fire recovery.
Advancing High-Resolution Surface Aboveground Biomass Modeling through Terrestrial Laser Scanning and Machine Learning in a Southeastern U.S. Pine Forest Ecosystem
Authors: Klauberg Carine , da Silva Gabriel Máximo , Loudermilk Eva Louise , Hawley Christie Stegall , Pokswinski Scott , Shimabukuro Yosio Edemir , Sánchez-López Nuria , Hudak Andrew , Silva Carlos Alberto
Published: March 2024

The integration of lidar (light detection and ranging) with machine learning offers a promising method for accurately estimating and mapping various vegetation attributes. This study demonstrated the effective use of terrestrial laser scanning (TLS) and the random forest (RF) machine learning approach to achieve precise total surface aboveground biomass (TSAGB) estimates at high resolution in regularly burned forest ecosystems in the southeastern United States. Our study site is located in the Osceola National Forest (ONF), which is part of the USFS Southern Research Station within the Olustee Experimental Forest, situated a short distance from Olustee, FL, and approximately 15 miles west of Lake City, FL. The site, spanning around 50 acres, was designated by the USFS Southeastern Forest & Range Experiment Station Southern Forest Fire Laboratory in 1957. Its primary purpose is to assess various fire return intervals and their impact on the accumulation of hazardous fuels. A total of 35 pre- burn clip plot (0.5m by 0.5m) samples in 2020 and 35 post-burn clip plot (0.5m by 0.5m) samples in 2022 were established for destructive TSAGB sampling. High-resolution 3D point cloud data were collected using the Riegl VZ 400i terrestrial laser scanner within each 5x5 meter collocated plot. A suite of cloud metrics was computed, and an RF model was fine-tuned for TSAGB, with a bootstrapping approach applied for model validation. The validation results indicated that a model utilizing only six metrics successfully predicted total TSAGB with relative and absolute Root Mean Square Error (RMSE) and bias of 57.59 g/m2 (32.78%) and -3.09 g/m2 (-1.76%), respectively. Our methodology, leveraging TLS lidar and widely used machine learning models, offers efficient solutions for enhancing the accuracy of surface biomass estimates in pine forests subject to frequent burns in the Southeastern U.S.
Investigating the Impact of Changing Ocean Ventilation, due to Climate Change, on the Biological Carbon Pump
Authors: Baltas Elisavet , Katavouta Anna , Hunt Hugh
Published: March 2024

There is compelling evidence that with increased warming the ocean is experiencing a number of changes to its circulation. For example, reduction in ocean ventilation due to increased stratification and the weakening of the Atlantic Meridional Overturning Circulation (AMOC). Consequently, this will alter carbon, oxygen, heat and nutrient distribution, and will therefore affect primary production and, by extension, the biological carbon pump. Due to the ocean’s huge capacity for carbon storage, it is imperative that we understand the consequences of these changes.
To investigate the effect of ocean ventilation on the biological carbon pump, two idealised box models for oceanic carbon and heat uptake are extended to include biological processes and nutrient cycling. The first model is a one-dimensional box model, with ocean ventilation being represented by a relaxation timescale that varies with increased warming from emissions. The second model is more complex, including a thermocline with a dynamically controlled thickness and meridional overturning circulation (that also varies with increased warming), which determines the extent of ocean ventilation.
These models have previously been used to carry out climate projections by investigating the ocean’s carbon and thermal response to annual anthropogenic emissions, but there has been no investigation on how changing circulation impacts the biological carbon pump. A simple nutrient-phytoplankton-zooplankton-detritus (NPZD) biological model is introduced to model the impact of macronutrient concentrations on phytoplankton and zooplankton growth. Using the extended models, we undertake simulations with both constant and changing circulation, in order to investigate the effect of slower circulation and increased ocean stratification on the biological carbon pump, primary production and nutrient distribution.
The DIMPLE Experiment to Date Ina, a Young-Looking Volcanic Structure on the Moon
Authors: Anderson F. Scott , Bierhaus Edward B. , Braden Sarah E. , Fagan Amy L. , Fausch Rico G. , Head III James W. , Joy Katherine H. , Levine Jonathan , Osterman Steve , Pernet-Fisher John , Tartèse Romain , Wurz Peter , Yant Marcella
Published: March 2024

            The DIMPLE (Dating an Irregular Mare Patch with a Lunar Explorer) experiment has been selected by NASA for flight to the Moon later this decade. The objective is to date volcanic rocks from Ina, the largest known (3×2 km) irregular mare patch. Ina is remarkable for its scarcity of impact craters; taken at face value, the crater density implies a surface model age of 33 ± 2 Ma [1]. If the Moon was volcanically active this recently, it would require a profound reassessment of our understanding thermal evolution of the lunar interior. An alternative explanation for the anomalously low crater density posits that Ina is a chilled magmatic foam [2], the vesicularity of which favors crumbling rather than cratering during meteoroid impacts. If vesicularity can make a ~3000 Ma old terrane appear to be 100× younger, it begs the question of what other surface age estimates based on crater density, from anywhere in the inner Solar System, can also be wildly inaccurate.              The DIMPLE payload includes (i) the CODEX (Chemistry, Organics, and Dating Experiment) instrument, (ii) a sample-handling system including an arm for gripping rocks off the lunar surface, and (iii) a rover-mounted rake for collecting rock samples from farther afield. CODEX works by analyzing hundreds of 35 μm spots over rock samples 1.9-3.8 cm across, using laser-ablation mass spectrometry to measure the abundances of major elements and some trace elements, and using laser-ablation resonance-ionization mass spectrometry to measure the isotopic abundances of Rb and Sr. In this lunar context, the organics capability of CODEX will not be exploited.  The mass spectrometer for CODEX is a re-flight of the mass spectrometer designed for the Luna-Resurs mission, with an optimized ion source to collect resonantly excited ions in addition to ions produced directly by laser ablation. CODEX data will enable dating by the 87Rb-87Sr isochron technique and, by mapping elemental composition, will permit lithologic classification and petrologic interpretation of the analyzed rock samples from Ina. References:        [1] Braden S.E. et al. (2014) Nature Geoscience 7, 787.                             [2] Qiao L. et al. (2021) Planet. Sci. J. 2 66.
Optimising oceanic rainfall estimates for increased lead time of stream level forecasting: A case study of GPM IMERG estimates application in the UK
Authors: Girotto Cristiane , Piadeh Farzad , Behzadian Kourosh , Zolgharni Massoud , Campos Luiza , Chen Albert
Published: March 2024

AbstractAmong the three main rainfall data sources (rain gauge stations, rainfall radar stations and weather satellites), satellites are often the most appropriate for longer lead times in real-time flood forecasting [1]. This is particularly relevant in the UK, where severe rainfall events often originate over the Atlantic Ocean, distant from land-based instruments although it can also limit the effectiveness of satellite data for long-term predictions [2]. The Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) estimates can be used as an alternative source for rainfall information in real-time flood forecasting models. However, the challenge lies in monitoring the vast oceanic region around the UK and integrating this extensive data into hydrological or data-driven models, which presents computational and time constraints. Identifying key monitoring area for obtaining these estimates is essential to address these challenges and to effectively use this use for water level forecasting in urban drainage systems (UDS).This study introduced an optimised data-driven model for streamline the collection and use of GPM IMERG rainfall estimates for water level forecasting in UDS. The model’s effectiveness was demonstrated using a 20-year satellite data set from the Atlantic Ocean, west of the UK, focusing on water level forecasting for a specific UDS point in London. This data helped identify the most probable path of rainfall from the Atlantic that impacts UDS water levels. We conducted a cross-correlation analysis between the water level records and each IMERG data pixel within the selected oceanic area.The analysis successfully pinpointed the most influential rainfall points/pixels along the Atlantic path and their respective lag times between rainfall occurrence and water level changes at any satellite-monitored point until it reaches the mainland and joins the river system. This research enhances understanding of long-distance rainfall patterns while optimising the use of GPM IMERG data. It also aids in reducing data volume and processing time for stream-level forecasting models, aiming for longer lead times. [1] Piadeh, F., Behzadian, K., Alani, A., 2022. A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.[2] Speight, L., Cole, S., Moore, R., Pierce, C., Wright, B., Golding, B., Cranston, M., Tavendale, A., Dhondia, J., Ghimire S. (2016). Developing surface water flood forecasting capabilities in Scotland: an operational pilot for the 2014 Commonwealth Games in Glasgow. Journal of Flood Risk Management, 11(S2), pp. S884-S901.
Constraining temperature variability projections using SMILEs that best represent observed variability
Authors: Maher Nicola , Suarez-Gutierrez Laura , Milinski Sebastian
Published: March 2024

Projecting how temperature variability is likely to change in the future is important for understanding future extreme events. This comes from the fact that such extremes can change due to both changes in the mean climate and its variability. The recent IPCC report found large regions of low model agreement in the change of temperature variability in both December, January, February (DJF) and June, July, August (JJA) when considering 7 Single Model Initial-Condition Large Ensembles (SMILEs). In this study we use the framework described by Suarez-Gutierrez et al, (2021) to constrain future projections of temperature variability by selecting the SMILEs that best represent observed variability.We consider 11 SMILEs with CMIP5 and CMIP6 forcing over 9 ocean regions and 24 land regions. We find that CESM2-LE, GFDL-SPEAR-MED and MPI-GE-CMIP6 perform best in DJF and CESM2-LE, CESM1-LE and GFDL-SPEAR-MED perform best in JJA. We find that the Southern Ocean is poorly represented in all models and that few models can represent Central America, the Amazon, North-East Brazil, and eastern and southern Asia, with western and eastern Africa poorly represented in JJA and northern Australia poorly represented in DJF. Overall models perform well over the Northern Hemisphere land masses. When we constrain temperature variability estimates, variability is generally lower, particularly over South America, Africa, and Australia – the same regions where the constraint improves projections, and where the projected change is smaller in the constraint. Where hot extremes increase so do cold extremes showing projected changes in all regions as a fattening or thinning of the distribution rather than a change in skewness.
Atmosphere aerosol over Ukraine and Kyiv city under Russian invasion 
Authors: Yukhymchuk Yuliia , Wei Xuanyi , Danylevsky Vassyl , Milinevsky Gennadi , Goloub Philippe , Fesianov Ihor , Syniavskyi Ivan
Published: March 2024

We investigate the impact of the Russian invasion and military activities on aerosol parameters in the atmosphere over Kyiv and Ukraine using data from the AERONET Kyiv station, the AirVisual network, and the MERRA-2 reanalysis. We examined the annual impact of the war on aerosol characteristics (Angstrom exponent, aerosol optical depth, fine particle fraction, aerosol absorption optical depth, complex refractive index) and PM2.5 specifically within the Kyiv city, using in situ observations from the AERONET and AirVisual networks. After the invasion, there was a short surge in PM2.5 contamination. We also investigated the levels of SO2, SO4, PM2.5 and black carbon before and at the onset of the full-scale invasion across Ukraine. The temporary improvement in air quality in Kyiv city is associated with a decrease in traffic load. AERONET observations reveal changes in the annual dynamics of the Ångström Exponent, with lower values observed in 2022 and a decrease in the fine aerosol fraction. Analysis of the aerosol complex refractive index indicates a shift in the dominant aerosol type present in the atmosphere. The AirVisual network data indicate no abnormal increases in PM2.5 concentrations during 2022 and early 2023, although elevated values were observed due to military activities and fires. The comparison of the state and properties of aerosol contamination before and after Russia's invasion shows a significant increase in the black carbon concentration in both the eastern and western parts of Ukraine.
Impacts of an upper tropospheric cold low on the extreme precipitation in Henan Province, China in July 202
Authors: Li Liangliang , Tian Wenshou , Li Jian , Huang Jinlong , Wang Rui , Luo Jiali
Published: March 2024

From 19 to 21 July 2021, Henan province of China experienced an extreme precipitation event that caused massive flooding and great loss of lives. This event is thus far the second heaviest precipitation event observed by rain gauges in this region. Based on the ERA5 reanalysis data, the ECMWF operational global ensemble forecasts and numerical simulations using the ARW-WRF model, impacts of an upper tropospheric cold low (UTCL) on the extreme precipitation are examined. It is found that due to the influence of the persistent intrusion of stratospheric high potential vorticity (PV) air, a long-lived UTCL was detached from the upper level flow a week prior to the extreme precipitation event. The UTCL then moved westward, reaching the Yellow Sea and the East China Sea and maintaining there until the precipitation event ended. During this event, a broad northeast-southwest oriented area of ascending motion associated with the UTCL could be observed in front of the UTCL and strong ascending motions developed in the upper troposphere above Henan province. Analysis of the ECMWF operational global ensemble forecasts reveals that the amount of precipitation over Henan is positively correlated with the UTCL intensity. The UTCL impact on the extreme precipitation and the underlying mechanisms are further investigated based on results of numerical experiments. The control experiment reasonably reproduces the UTCL location as well as the distribution and evolution of the extreme precipitation. When the UTCL intensity is reduced in the initial condition using the piecewise PV inversion for sensitivity experiment, the upper tropospheric divergence reduces correspondingly and the dynamical ascending motion weakens in the second precipitation stage. As a result, the amount and intensity of precipitation both decrease. When the UTCL is completely removed from the initial condition, the sensitivity experiment indicates that the upper tropospheric divergence and dynamical ascending motion further weaken, resulting in a large decrease in precipitation intensity during the whole precipitation period. These findings highlight that the occurrence of the long-lived UTCL is a crucial factor that affects the intensity of the extreme precipitation event.
Detecting Disturbance to Agricultural Productivity from Historical Armed Conflict in Afghanistan: The Panjshir Offensives, 1980-1985
Authors: Allen Jeremy
Published: March 2024

A particular challenge threatening global food security is the threat of armed conflict. In particular, the Panjshir valley of northeastern Afghanistan continues to experience acute food insecurity due to intense armed conflict. In this rural valley, conflict driven displacement leads to agricultural land abandonment and decreases in crop yields. These decreases in local food production have an outsized impact on food security, due to the region’s dependence on subsistence agriculture. Despite the consensus that armed conflict has a significant negative impact on the population’s food security, the exact mechanics of how conflict impacts food security remains unclear.  To quantify armed conflict’s impact on local food production, I compare trends in vegetation health between agricultural plots in high-conflict and no-conflict landscapes with similar altitudinal gradients. I focus on the period during the Soviet occupation of Afghanistan from 1980-1989, which saw nine major military offensives occur in the Panjshir valley. I use Landsat 5 (1984-2012) to obtain the Normalised Difference Vegetation Index (NDVI) values for agricultural plots that have been designated as control (no conflict) and treatment (high conflict). These plots are delineated using HEXAGON KH-9 declassified spy imagery, and assigned conflict intensity designations based on explosive ordnance disposal (EOD) data from The HALO Trust, a non-governmental organisation which carries out unexploded ordnance clearance in Afghanistan. Residual Trend analysis (RESTREND) is applied to Landsat NDVI values to distinguish between the shifts in vegetation health that are anthropogenically and climatically driven.  This research provides a deeper understanding of how past conflict has acted as a driver of food insecurity in the region. Additionally, it allows for future work to build off of these findings and predict how current and future conflict might have an impact. These findings can inform humanitarian and development aid policy, while the methodology can be applied to other contexts where conflict is present. 
Insights on collisionless shock physics from the Magnetospheric Multiscale (MMS) mission at Earth
Authors: Turner Drew
Published: March 2024

We briefly summarize some of the latest, impactful results concerning the nature and physics of collisionless shock waves as observed by NASA’s Magnetospheric Multiscale (MMS) mission. MMS routinely observes Earth’s bow shock and also infrequently captures interplanetary shocks at 1 AU. With the four, identical observatories outfitted with a comprehensive payload of state-of-the-art plasma instrumentation, MMS offers unprecedented capabilities for studying the micro-to-global-scale nature of shocks in near-Earth space. New results highlighted include: the global energy budget and energy partitioning at Earth’s bow shock; complexity and significance of the quasi-parallel shock regime and the ion foreshock;  the acceleration of ions to >1 MeV and electrons to relativistic (>100 keV) energies; and kinetic-scale dynamics of shock fronts including reconnection and We finish with new results of an ongoing large-scale, statistical study of Earth’s bow shock empowered by machine learning applied to the full MMS dataset. We finish with a discussion of the upcoming MMS orbital configurations in regards of new studies plus idealized concepts for future missions to study collisionless shocks.
 Change Ratios of Magnetic Helicity and Magnetic Free Energy During MajorSolar Flares 
Authors: Wang Quan , Yang Shangbin , Zhang Mei , Yang Xiao , Zhu Xiaoshuai
Published: March 2024

Magnetic helicity is an important concept in solar physics, with a number of theoretical statements pointing out the important role of magnetic helicity in solar flares and coronal mass ejections (CMEs). Here we construct a sample of 47 solar flares, which contains 18 no-CME-associated confined flares and 29 CME-associated eruptive flares. We calculate the change ratios of magnetic helicity and magnetic free energy before and after these 47 flares. Our calculations show that the change ratios of magnetic helicity and magnetic free energy show distinct different distributions in confined flares and eruptive flares. The median value of the change ratios of magnetic helicity in confined flares is −0.8%, while this number is −14.5% for eruptive flares. For the magnetic free energy, the median value of the change ratios is −4.3% for confined flares, whereas this number is −14.6% for eruptive flares. This statistical result, using observational data, is well consistent with the theoretical understandings that magnetic helicity is approximately conserved in the magnetic reconnection, as shown by confined flares, and the CMEs take away magnetic helicity from the corona, as shown by eruptive flares.
How do inferred statistical properties of switchbacks depend on their definition?
Authors: Das Srijan Bharati , Badman Samuel
Published: March 2024

Deflections in magnetic fields accompanied by Alfvenic velocity fluctuations are observed in the solar wind and have been termed as switchbacks. While signature of switchbacks have been reported from analysis of Ulysses, Wind and Helios satellites at distances of distances of about 2.4 A.U, 1.0 A.U and 0.3 A.U respectively, since 2018, Parker Solar Probe (PSP) has been providing us a wealth of in-situ measurements within as close as 10 solar radius. Studies on identification of SBs and its properties have boomed in the era of PSP and Solar Orbiter. Open questions regarding the origin of SBs exist which require rigorous quantification of SB properties. However, in order to assess properties across SB events, choices have to be made for how to categorize SBs based on classifying criteria for example, the extent of deflection of the magnetic field compared to the background field (commonly known as the Z-angle), the duration and strength of field or velocity fluctuations during the SB event. A key feature that also requires quantification when classifying SBs is what constitutes the quiet background --- currently common choices include using a running average to build a smooth background and picking out deflections about the smoothened fields or choosing the Parker spiral to construct the background fields. By picking out specific criteria for classifying SBs and analyzing common time periods on the same SB definitions, we investigate to what extent inferred properties depend on the chosen definitions.
What determines the predictability of a Mediterranean cyclone?  
Authors: Doiteau Benjamin , Pantillon Florian , Plu Matthieu , Descamps Laurent , Rieutord Thomas
Published: March 2024

Cyclones provides the majority of water supplies in the Mediterranean and are essential elements of the climate of the region. The most intense of them lead to natural disasters because of their violent winds and extreme rainfall. Identifying systematic errors in the predictability of Mediterranean cyclones is therefore essential to better anticipate and prevent their impact. The aim of this work is to understand what processes determine their predictability. We investigate the predictability of Mediterranean cyclones in a systematic framework using an ensemble prediction system. First, a reference dataset of 2853 cyclones is obtained by tracking lows in the ERA5 reanalysis, using an algorithm developed for the North Atlantic and adapted for the Mediterranean region. Then we investigate their predictability using IFS ensemble reforecasts in a homogeneous configuration over 22 years (2000-2021). The predictability in the reforecasts is quantified using probabilistic scores on cyclones trajectories and on intensity (mean sea level pressure) and then crossed with explanatory variables such as geographic area, cyclone velocity, season and intensity.The evolution of location error with lead time shows a two phases growth, until and beyond 72 h, which will be discussed. When crossing the location and intensity errors with the explanatory variables, we can identify the conditions leading to a poorer (respectively better) predictability. In particular the velocity of cyclones appears to play an important role in the predictability of the location, the slower the cyclone the better the predictability, while the season is shown to play a greater role on the predictability of the intensity. These characteristics are also dependant on the sub-region considered and on the intensity of the low itself, the deeper the cyclone, the poorer the predictability in both the location and the intensity.
What we need in thermal infrared (TIR) data to forecast volcanic activity: From new ground-based sensors to a rapid-revisit orbital concept
Authors: Ramsey Michael , Thompson James , Smekens Jean-Francois
Published: March 2024

Thermal monitoring of volcanic activity has become common over the past 25 years and is well integrated into the set of tools at several volcano observatories. These data are acquired from a range of sensors including permanent ground-stations, less frequent campaign mode deployments from the ground and air, as well as orbital remote sensing. The fundamental ability to forecast a new eruption using orbital TIR data remains aspirational despite decades of data acquisition, modeling, and analysis. In contrast, large-scale thermal change detection is routine and used to rapidly identify a new eruption and monitor its evolution. Sensors with lower spatial (≥ 1 km) and higher temporal (≤ 24 h) resolutions are best suited for acquiring these data and provide near-real time information. Our recent work examines higher spatial, lower temporal resolution low-Earth orbit data to identify precursory thermal eruption signals. Foundational to this is the ability to retrieve accurate subtle (1-2 K) temperature changes, which are easily overlooked using current change detection approaches. Long time series orbital TIR data enable a unique opportunity to quantify these low-level anomalies and small eruption plumes over long periods. Most significant is the finding that the smaller, subtle detections served as precursory signals in ~81% of eruptions. Over the next decade, several high spatial (~ 60 m) resolution orbital sensors are planned that provide near-daily TIR data at every volcano, vastly improving thermal baselines and detection of new activity. One of these, the Surface Biology and Geology (SBG) mission, contains an infrared instrument, which also plans volcano-specific data products that are crucial for accurate daily monitoring of volcanic temperatures and degassing rates. In preparation for the SBG mission’s Volcanic Activity (VA) data product, we have developed a low-cost, ground-based, multi-wavelength TIR sensor known as the MMT-gasCam. NASA has funded a plan to use this instrument to measure small, passive plumes in emission to determine the detection threshold of sulfur dioxide in the TIR, its conversion to sulfate aerosol, and the temperature of the emitted gas/vent in volcanic plumes. Results will help to validate the future VA data product. However, despite the promise of SBG data, the fundamental step-change in orbital volcanology will not come until high-speed orbital data are possible. A proposed hypertemporal TIR mission would acquire these data at sub-minute scales to determine mass and thermal flux rates of gas emissions, eruptive ash plumes, and lava flows. With such a mission, data now acquired by current ground-based cameras will become possible from orbit for the first time.
Derivation of wave speed for dry and saturated nonlinearly elastic models
Authors: Riley David , Einav Itai , Guillard Francois
Published: March 2024

Elastic wave speeds are crucial in geomechanics for measuring elastic stiffness. Traditionally, these speeds are calculated using an analytic formula based on a linearly elastic solid medium. However, empirical evidence suggests that stiffness is state-dependent, creating a mismatch between the theoretical assumptions and the observed stiffness constants. Recent advancements have addressed this gap by deriving wave speeds for hyperelastic (energy conserving) and hypoelastic (non-energy-conserving) constitutive models that are dependent on pressure and density. These new derivations align with conventional empirical findings for isotropic states. However, the hyperelastic model predicts variations in the ratio of longitudinal to transverse wave speeds, as observed in experiments and discrete element simulations. This prediction emerges from energy-conservation terms in the model, eliminating the need for fabric assumptions previously considered in research. Our study expands on this by exploring various density-dependent relationships and extending wave speed calculations to saturated granular media. This research enhances our understanding of granular media stiffness in dry and saturated scenarios, offering new insights for interpreting wave speed experiments.
Application of a Neural Network Algorithm to Estimate the Nutrients Concentration in the Peruvian Upwelling System
Authors: Asto Cristhian , Bosse Anthony , Pietri Alice , Colas François , Sauzède Raphaëlle , Gutiérrez Dimitri
Published: March 2024

The Peruvian coastal upwelling system (PCUS) is one of the most productive in the world ocean. The Peruvian Marine Research Institute (IMARPE) has been monitoring the PCUS  since the 1960’s with an increase in the frequency and spatial distribution of measurements since the early 2000’s focusing on temperature, salinity and oxygen. In recent years, autonomous gliders have started to be routinely deployed by IMARPE, collecting a large amount of profiles. However, there is still a gap for the high-resolution  sampling of biogeochemical parameters such as nutrients (nitrate, phosphate and silicate).New  methods using machine learning to reconstruct missing data have been developed recently with promising results (Sauzède et al, 2017; Bittig et al., 2018; Fourrier et al., 2020). In particular, a recent global approach using neural networks (NN) named CANYON-B (CArbonate system and Nutrientes concentration from hYdrological properties and Oxygen using a Neural network) was developed in order to fill those gaps and infer nutrients’ concentrations from the more sampled variables of temperature, salinity and oxygen (Bittig et al., 2018).In this work we show the application of this global CANYON-B algorithm to the PCUS using all the historical IMARPE’s CTD casts. Moreover, we trained a new NN and compared its outputs with the ones from CANYON-B, demonstrating the benefits of training the NN with the extensive regional data set collected by IMARPE.
Multi-phase simulation of gravel beach morphodynamics
Authors: Lee Cheng-Hsien , Cheng Hao-Yu
Published: March 2024

This study applies an Eulerian–Eulerian multi-phase model to investigate sediment transport dynamics across a gravel beach subjected to regular waves. We numerically replicated a previously conducted full-scale experiment, achieving consistency between simulated and measured wave deformation, flow velocity, pore pressure, and morphological changes. The multi-phase model successfully reproduced observed phenomena, demonstrating berm formation for smaller waves and beach erosion for larger waves, aligning with findings from prior field studies. A sensitivity analysis reveals the substantial influence of the nonlinear drag component on berm formation, primarily through infiltration and exfiltration processes. Two dominant forces acting on sediment, namely drag and buoyancy, are studied. Drag emerges as the primary force governing sediment transport, exhibiting a strong correlation with depth-averaged flow velocity. The buoyancy, generated by plunging flow impacting the beach face, propels sediment landward in the bore front, coinciding with peak flow acceleration. Previous sediment transport models have empirically considered flow acceleration for gravel beach morphological evolution under waves. This study establishes a linkage between buoyancy, flow acceleration, and sediment transport rate in the bore front, offering valuable insights into the intricate interactions governing gravel beach dynamics. The findings contribute to our understanding of sediment transport mechanisms and hold implications for coastal engineering.
Evaluating the magnetic and plasma flux transport in the plasma sheet during geomagnetic storms using MMS
Authors: Raptis Savvas , Merkin Slava , Ohtani Shin , Gkioulidou Matina
Published: March 2024

During geomagnetic storms, the nightside of Earth’s magnetosphere experiences significant disturbances, featuring various phenomena that interact with one another. These include enhanced convection and particle injections influencing ring current development, alongside mesoscale structures like bursty bulk flows (BBFs) and dipolarization fronts, which play roles in plasma and magnetic flux transport within the plasma sheet. However, their specific functions during storms are not fully comprehended.In our work, we evaluate the transport of magnetic and plasma flux in the plasma sheet region using data obtained from NASA’s Magnetosphere Multiscale (MMS) mission. Leveraging MMS's extensive measurements from the plasma sheet, a statistical analysis is conducted based on nearly 200,000 data points (at 4.5-second resolution) obtained from the Fast Plasma Investigator (FPI) during quiet and disturbed geomagnetic periods.The statistical examination primarily focuses on how different properties describing convection in the plasma sheet vary across distinct phases of storms (main and recovery) and at various distances from Earth. Furthermore, we compare our results with these obtained by prior research from other missions. Additionally, we assess whether variations in instrumental capabilities, such as time resolution and energy range among different instruments on MMS, could influence the statistical outcomes
Advances in Cosmogenic Nuclides measured in Ferrar Pyroxene
Authors: Bergelin Marie , Balco Greg
Published: March 2024

Measurement of multiple cosmogenic nuclides in single samples is valuable for various applications. For exposure dating, multiple-nuclide systematics are important for correcting exposure ages for surface weathering and erosion, as well as quantifying when and how often a surface has experienced burial. Currently, such constraints from paired 26Al/10Be/21Ne measurements in quartz are common practice and well established. However, 36Cl/10Be/3He nuclide measurements in pyroxene would provide more precise constraints because the range in decay constants is greater. In Antarctica, the pyroxene-bearing Ferrar Dolerite is one of the dominant lithologies of clast exposed at the surface of many surficial deposits throughout the Transantarctic Mountains. While cosmogenic 3He measurements in Ferrar pyroxene are routine, 10Be and 36Cl measurements are less common and not well established. Here we present recent advances in multiple cosmogenic nuclides measured in the Ferrar pyroxene, which includes (i) improvements in the extraction of 10Be and 3He from pyroxene grains, (i) production rate estimates of 10Be in pyroxene and 36Cl from Ca spallation, and (iii) understanding and quantifying the non-cosmogenic inventories of 3He and 36Cl in pyroxene for accurate exposure dating.
Characterizing Uncertainty in Spatially Interpolated Time Series of Near-Surface Air Temperature
Authors: Doherty Conor , Wang Weile
Published: March 2024

Spatially interpolated meteorological data products are widely used in the geosciences as well as disciplines like epidemiology, economics, and others. Recent work has examined methods for quantifying uncertainty in gridded estimates of near-surface air temperature that produce distributions rather than simply point estimates at each location. However, meteorological variables are correlated not only in space but in time, and sampling without accounting for temporal autocorrelation produces unrealistic time series and potentially underestimates cumulative errors. This work first examines how uncertainty in air temperature estimates varies in time, both seasonally and at shorter timescales. It then uses data-driven, spectral, and statistical methods to better characterize uncertainty in time series of estimated air temperature values. Methods for sampling that reproduce spatial and temporal autocorrelation are presented and evaluated. The results of this work are particularly relevant to domains like agricultural and ecology. Physical processes including evapotranspiration and primary production are sensitive to variables like near-surface air temperature, and errors in these important meteorological inputs accumulate in model outputs over time.
Development of the electron streaming instability in magnetopause reconnection
Authors: Tang Binbin , Li Wenya , Wang Chi
Published: March 2024

The development of the electron streaming instability is usually very fast, making it difficult to be observed from spacecraft observations. However, with a separation of 10s of km, Magnetospheric Multiscale(MMS) spacecraft provide a good opportunity to resolve the spatial evolution of election beams and also the corresponding electron streaming instability. Here, we show such an event in the magnetopause reconnection, in which the accelerated electrons of the sheath origin become unstable at the magnetospheric side, and different MMS spacecraft captures significantly different wave activities. Interestingly, the scattered electrons may even form agyrotropic electron distributions as observed by one MMS spacecraft.
 