bluuebunny/crossref_metadata_2025 huggingface dataset:
Compound Flood Potential from Co-occurrence of River Discharge and Storm Surge in Croatia
Authors: Krvavica Nino , Bilić Marta Marija , Ružić Igor
Published: March 2024

Coastal areas are becoming increasingly vulnerable due to climate change. These regions are exposed to various sources of flooding, such as high sea levels, river discharge and heavy rainfall. Our study focuses on understanding compound flooding from storm surges and river discharge in Croatia. This is the first study on compound floods in this country. For this purpose, we analysed the time series of water levels and discharges from hydrological stations located along ten major coastal rivers. Since there are only a limited number of tide gauges in Croatia, we combined measured data with numerical reanalyses. The sea level data for the entire Adriatic Sea were obtained from the Copernicus Marine Service (Mediterranean Sea Physics Reanalysis) and were then corrected using machine learning and measured data.Previous studies have shown that neglecting seasonal variations in river discharge and storm surges could lead to a significant underestimation of the expected annual damage from compound floods. Different seasons bring distinct weather and river discharge patterns that influence the probability and severity of compound floods. To address this, our study investigated seasonal correlation and co-occurrence by analysing the monthly maximum values. By examining each season in detail, we uncovered the variations in the compound flood potential index.This analysis provides a more comprehensive understanding of compound floods in Croatia, which is crucial for risk assessment and risk management. Finally, we mapped the correlation coefficients, the number of co-occurrences and the compound flood potential index along the Croatian coast and organised the results in a GIS database. These maps will improve our ability to systematically select the most vulnerable areas where the risk of compound flooding should be analysed at the local level.
Unveiling the early Eurasian glaciations with new advances of 10Be-26Al burial dating
Authors: Ylä-Mella Lotta , Wagner Kaleb , Margold Martin , Knudsen Mads Faurschou , Busschers Freek , Bakker Marcel , Wachecka-Kotkowska Lucyna , Krzyszkowski Dariusz , Wieczorek Dariusz , Szuman-Kalita Izabela , Eriksen Birte Lindahl , Andersen Jane Lund , Olsen Jesper , Perić Zoran , Alexanderson Helena , Jansen John D.
Published: March 2024

Cosmogenic nuclide burial dating exploits the differential decay rate of a nuclide pair, typically 26Al and 10Be. There are three basic approaches; simple burial dating, the isochron method and P-PINI (Particle Pathway Inversion of Nuclide Inventories). The simple burial age is based on deviation from the surface production ratio assuming quick and deep burial. The isochron method incorporates multiple samples from a single stratigraphic layer and assumes steady erosion in the sediment source area. P-PINI accommodates more complicated situations, such as landscapes undergoing non-steady erosion and an eroding or accumulating sink zone. This source-to-sink model uses a Monte Carlo simulator to create a library of plausible erosional and depositional histories and then compares those histories against measured nuclide data to derive a burial age estimate. In cases involving multiple dated layers in a section or when an independent age estimate is available, a Bayesian tool further constrains the burial age.With the aim of constraining the timing of the Eurasian Ice Sheet at its largest known extent, we applied P-PINI at Wapenveld in the Netherlands and at Szczerców in central Poland. From a drill-core at Wapenveld we collected three sand samples from the Early Pleistocene ‘Hattem’ beds, and derived the P-PINI burial age bracketed by simple burial ages calculated from the units above and below. Szczerców is an open-cast mine exposing multiple Middle Pleistocene till units rich in cobble-sized clasts. Here, P-PINI burial ages show sensitivity to the thickness of the dated layer, a problem we resolve by including feldspar infrared-simulated luminescence dates higher up the sequence.We discuss the pros and cons of our approach at Wapenveld and Szczerców. Burial dating multiple units in section requires considerably more lab effort but also offers important advantages that can be boosted by carefully considering which grain-size to sample, and by incorporating independent age constraints.
Ice production in northern hemisphere cold air-outbreak clouds: two contrasting aircraft campaigns
Authors: Murray Benjamin ,   M-Phase Team
Published: March 2024

Cold-air outbreaks (CAOs) are common high-impact weather events that produce extensive boundary layer clouds that have a substantial influence on our planet’s climate. These clouds are often supercooled and therefore their properties are susceptible to the formation of ice.  The amount of ice in these clouds has been identified as being particularly important for defining the magnitude of the cloud-climate feedback and climate sensitivity.To address ice production in northern hemisphere CAOs we conducted two contrasting aircraft campaigns in 2022.  One campaign (ACAO, 11 flights) was in March in the Norwegian and Barents Sea where cold air flowed from the ice-covered Arctic Ocean. The other (M-Phase, 12 flights) was in October-November and focused on the Labrador Sea with air coming from the Arctic Archipelago. In both campaigns, we used similar instruments on the FAAM BAe-146 research aircraft designed to probe the aerosol properties, cloud microphysics and atmospheric thermodynamics of the CAO events.  Flight sorties were designed to study aerosol-cloud interactions as the CAO developed through the stratus and into the cumulus regime.We found that INP concentrations in these Northern Hemisphere CAOs were orders of magnitude greater than CAO events over the Southern Ocean.  The springtime ACAO cases had systematically greater INP (and aerosol) concentrations than the autumnal Labrador Sea M-Phase cases. The presence of substantial amounts of mineral dust in the springtime Arctic, despite all local sources being covered in ice and snow, implies a reservoir of old INPs and aerosol in the springtime Arctic that originated from the low latitudes. This is supported by our global aerosol model. Primary ice production by INPs is shown to define the ice concentrations in the stratus regime in many cases, but in the cumulus regime there are pockets of very high ice concentrations that are indicative of secondary ice production.Our modelling work has demonstrated that INPs are key to defining the stratus to cumulus transition and the cases are providing an excellent test for the high-resolution regional modelling with the Met Office Unified Model.  We are also using ACAO cases to study how INPs interact with clouds in CAOs, where warm temperature INPs are preferentially lost through nucleation scavenging. Furthermore, we envisage that the data from these campaigns will provide a valuable resource for model development, hypothesis testing and contrasting with other CAO campaigns in other places and times. Given the stark contrast of primary ice production in CAO clouds in different locations and times around the globe, we conclude that the primary production of ice in model CAO clouds should be linked to the aerosol properties and knowledge of the local INP population to reduce uncertainty in cloud feedback and climate sensitivity.
Incorporating Data Assimilation into Land Surface Model simulation for better estimation of Surface Soil Moisture over India
Authors: Chakraborty Arijit , Saharia Manabendra , Chakma Sumedha , Kumar Sujay V. , Getirana Augusto
Published: March 2024

Soil moisture is a significant environmental factor that influences both the water and energy balance at the land-atmosphere interface. Therefore, proper assessment of the spatial and temporal distribution of soil moisture is crucial for many hydrological applications such as weather forecasting, agricultural water resource management and drought monitoring. This study involves the assimilation of Soil Moisture Active Passive (SMAP) soil moisture dataset within a land surface model and the evaluation of its performance in precise estimation of soil moisture by comparing the statistics with respect to standard European Space Agency’s Climate Change Initiative (ESA-CCI) soil moisture dataset. The Ensemble Kalman Filter technique has been used for assimilating SMAP soil moisture data using Noah-MP land surface model within NASA Land Information System (LIS) framework. The data assimilation (DA) framework includes Cumulative Distribution Function (CDF) matching for bias correction and twenty ensembles per tile. Meteorological forcings for the simulations have been taken from MERRA2 and IMD. Improvement or degradation due to DA has been analyzed in terms of the difference in anomaly correlation between open loop (OL) and DA soil moisture outputs with respect to the ESA-CCI soil moisture dataset over the entire Indian domain. The DA result shows improvement over larger areas in the case of MERRA2 forced simulations than IMD+MERRA2. The seasonal impact of DA in terms of the differences in DA and OL simulated soil moisture shows less variability in summer than winter. The results are validated with in-situ soil moisture datasets. Overall, the study shows that data assimilation is giving better results than open loop LSM simulation, which can be used for improved estimation of other water balance components.
Improving the representation of surface-groundwater and human-water interactions in a coupled surface-subsurface water model
Authors: Zheng Yanchen , Coxon Gemma , Rahman Mostaquimur , Woods Ross , Salwey Saskia , Wendt Doris
Published: March 2024

Groundwater is a vital component of the hydrologic cycle and also the largest human and ecosystem accessible freshwater storage, which plays an important role in many hydrological processes. However, in groundwater-dominated catchments where inter-catchment groundwater flow through subsurface flow pathways, most hydrological models lack explicit representations of these transboundary surface-subsurface interactions, resulting poor performance in hydrological predictions. Additional complexity introduced by intense groundwater abstractions and management schemes are also poorly represented in current hydrological models, which hinders accurate hydrological simulations. Therefore, developing integrated modelling frameworks for simulating the interactions between surface water, groundwater and human influences is needed for accurate hydrological predictions in these regions.DECIPHeR is a flexible hydrological modelling framework, which has demonstrated its good performance across a diverse range of catchments in Great Britain. However, in groundwater-dominated catchments, enhancements are needed in representing surface-subsurface water interactions for better model performance. This study integrates a national-scale groundwater model into DECIPHeR. We will utilize observational hydro-meteorological data to calibrate and validate the coupled model across 475 catchments. Additionally, a large sample of groundwater level data (over 3000 sites) in England will be used to further evaluate the model. Initial tests show that the coupled model outperforms DECIPHeR in Chalk catchments and also performs well (KGE>0.6) in other geology. The coupled models enable the assessment of surface-groundwater impacts, facilitating the potential quantification of human-water interactions, i.e. groundwater abstractions, on hydrological simulations. This analysis aims to support effective water supply and demand management strategies across Great Britain by providing insights into the influence of surface-groundwater interactions on the hydrological system.
Performance of Global Wind Atlas for Distributed Wind Resource Assessment in the United States
Authors: Sheridan Lindsay , Preziuso Danielle , Phillips Caleb , Duplyakin Dmitry , Tinnesand Heidi
Published: March 2024

Distributed wind projects, particularly those involving small wind turbines, are more subject to financial and temporal limitations than utility-scale wind energy. Onsite measurements are often not feasible or economically viable investments, leading to developers, analysts, and customers in the distributed wind community relying on wind resource models to establish generation estimates. One popular wind product used by the distributed wind community in the United States is the global, high-resolution Global Wind Atlas from the Technical University of Denmark and the World Bank Group.Wind resource models are valuable tools for siting and establishing generation expectations but are not entirely accurate, which can lead to distributed wind customer dissatisfaction when actual energy generation does not meet pre-construction expectations. To enhance the understanding of the performance and limitations of utilizing Global Wind Atlas for wind resource assessment, this work presents the validation of the model wind speeds using meteorological towers across the diverse geography of the United States with measurement heights relevant to distributed wind hub heights (20 m – 100 m). The analysis expands to quantify the performance of Global Wind Atlas in representation of seasonal, diurnal, and interannual variability in the wind resource along with an assessment of wind shear accuracy at locations with measurements at multiple heights.
Microbial nitrogen cycle in sub-tropical peatland cloud forest and wetland ecosystems of Réunion Island
Authors: Kazmi Fahad , Mander Ülo , Ranniku Reti , Öpik Maarja , Soosaar Kaido , Kasak Kuno , Ah-Peng Claudine , Espenberg Mikk
Published: March 2024

Peatlands play an essential role in the regulation of carbon and nitrogen cycles. Nitrogen-rich peatlands under warm and dry conditions can be a source of N2O, a potent greenhouse gas. Research on microbial activity, particularly in relation to N2O emissions in sub-tropical peatlands and wetlands, is very limited. In the current study, we investigated two peatland cloud forest sites in Reunion Island, namely Plaine des Cafres (characterized by dominant species Erica reunionensis and Alsophila glaucifolia) and Forêt de Bébour (featuring Erica reunionensis exclusively), alongside one RAMSAR wetland site located in Saint Paul. Both cloud forest sites were located at an altitude of 1500-1600 m, while the wetland was at an altitude of 4 m. to clarify the microbial dynamics of the nitrogen cycle in sub-tropical peatland cloud forests and wetlands.DNA extraction was performed on these samples, followed by quantification of genes associated with the nitrogen cycle using quantitative polymerase chain reaction (qPCR). Analyses are ongoing for plant samples. In addition, soil samples underwent analyses to assess levels of ammonium (NH4+-N) and nitrate (NO3−-N). Soil N2O fluxes were determined by collecting gas samples from the chamber headspace of static soil chamber systems at 20-minute intervals during one-hour sessions. The concentration of N2O was determined from gas samples using a gas chromatographer (Shimadzu, 2014).All sites emitted negligible soil N2O fluxes (mean: 0.9 µg N m−2 h−1). However, a substantial amount of soil NH4+-N was found across all sites (mean: 77.2 mg/kg). The forests dominated by Erica reunionensis showed the highest values (mean: 106 mg/kg). Soil NH4+-N significantly correlated with the abundance of the nifH gene (R2 = 0.7, p
Distinguishing between cloud and aerosol layers in the TROPOMI/Sentinel-5P measurements
Authors: Argyrouli Athina , Lutz Ronny , Romahn Fabian , Molina García Víctor , Lelli Luca , Loyola Diego , Torres Omar , Marinou Eleni , Amiridis Vassilis
Published: March 2024

TROPOMI on board of Sentinel-5 Precursor (S5P) provides continuous daily distribution of several cloud properties, which are required as input for trace-gas retrievals. The operational TROPOMI cloud retrieval is a two-step algorithm. At first, the OCRA (Optical Cloud Recognition Algorithm) computes a radiometric cloud fraction using a broad-band UV/VIS color space approach and later the ROCINN (Retrieval of Cloud Information using Neural Networks) retrieves the cloud height, cloud optical thickness and cloud albedo from NIR measurements in and around the oxygen A-band (~760nm). Within the ROCINN algorithm two different models are possible; the Clouds-as-Reflecting-Boundaries (CRB), where the cloud is a simple Lambertian reflector and the Clouds-as-Layers (CAL), where the cloud is a homogeneous layer of scattering liquid-water spherical particles. There is evidence that some TROPOMI cloud retrievals are contaminated by aerosols. This is particularly true in the following cases: (a) when there is co-existence of clouds and aerosols in the same TROPOMI footprint and (b) when there is a pure aerosol layer, appearing in the TROPOMI cloud product. The latter is usually the case of OCRA deriving an elevated radiometric cloud fraction corresponding to the given aerosol conditions. Then, ROCINN is triggered and returns two additional cloud parameters. Often, the false alarms of elevated OCRA cloud fraction can be identified when ROCINN retrieves a cloud height at the surface level. However, there are cases in which ROCINN cloud outputs do not refer to the surface properties of the scene, but to aerosol layers present in the same TROPOMI footprint. Especially for dust aerosols, which are usually large particles and comparable to the cloud droplet size, we expect more frequently those mixed retrievals. In particular, dust layers with large concentrations (i.e., high aerosol optical depth (AOD)) are better candidates for erroneously retrieved clouds in the TROPOMI L2 product. The TROPOMI aerosol algorithm (TropOMAER) makes use of the L1b reflectances in the UV to derive aerosol information in cloud-free and above-cloud aerosol scenes. With the use of ground-based active and passive remote sensing instruments, we are able to characterize well the vertically resolved cloud and aerosol layers in the lower troposphere. In this work, synergistic ground-based measurements from a PollyXT multiwavelength-Raman-polarization lidar and an AERONET sun-photometer are used to discriminate dust aerosols from clouds in TROPOMI measurements. We have selected ground-based observation sites over which the atmospheric column frequently contains large contributions of desert dust particles.
Medium-Range Excessive Rainfall Prediction with Machine Learning
Authors: Hill Aaron , Schumacher Russ
Published: March 2024

The prediction of excessive rainfall using numerical weather prediction (NWP) models is unequivocally difficult owing to the myriad of complexities that must be resolved (e.g., parent storm dynamics, microphysics) in order to forecast the placement and intensity of rainfall correctly. However, machine learning (ML) has provided a new avenue by which we can generate predictions of excessive rainfall with sufficient lead time to inform decision makers and planners to the threat of inclement weather. ML techniques are able to decode known long-standing relationships between environmental predictors and convective hazards from long historical records, and they have demonstrated tremendous value in predicting weather hazards at longer lead times (e.g., Hill et al. 2023). Further, continued effort by the meteorological community to explain ML models and their forecasts is building trust between developers and end users. As a result, their use in meteorological hazard forecasting is expanding, particularly into the medium range (e.g., 4-8 days) when forecasters are reliant on relatively coarse NWP models to create forecasts. In this work, we are using Random Forests (RFs) to generate daily probabilistic forecasts of excessive rainfall at 1-8 day lead times. The RFs are trained using output from the Global Ensemble Forecast System and historical observations of excessive rainfall. Environmental parameters like precipitable water and CAPE, as well as modeled precipitation, are spatiotemporally arranged so the RFs can learn spatial and diurnal patterns that associate with excessive rainfall. The RF models are evaluated against a spatio-temporally varying climatology and show skill out to 7 days, and routinely outperform human-based forecasts past a 1-day lead time. In this presentation, we will highlight performance characteristics of the RFs into the medium-range (e.g., out to 8 days) and discuss the implications of excessive rainfall definitions in RF model training. Additionally, we will present an ensemble prediction framework that provides estimates of uncertainty and ranges of forecast solutions that operational forecasters desire at extended lead times.
Towards a machine learning model for data assimilation and forecasting directly trained from observations
Authors: Lessig Christian , Lean Peter , McNally Tony , Alexe Mihai , Lang Simon , Chantry Matthew , Dueben Peter
Published: March 2024

State-of-the-art data assimilation systems, such as the 4DVar system of the European Centre for Medium-Range Weather Forecasts (ECMWF), are highly successful in producing state estimates of the atmosphere constrained by millions of observations. However, existing systems require substantial approximations, e.g. in forward operators, and employ conventional models in their optimization loop. This limits the amount of information that can be extracted from observations, e.g. the assimilation of visible satellite channels is still challenging. The current approach also separates the use of observations into a data assimilation step and a forecasting one, with observations only being used indirectly for forecasting, for example for tuning of parametrizations and for evaluation.Here, we explore the possibility to train large machine learning models for data assimilation and forecasting directly from observations. In particular, we build a generative transformer neural network that models the joint the probability distribution p(y,x) over output states y for an input x. The input are observations from a temporal window, e.g. 6h or 12h, and the output y can either be an estimate of the state within the window or a short-term forecast, e.g. for another 12h. Different observations are processed by different embedding networks but then fused in the backbone transformer network. To obtain an integrated and consistent representation of the atmospheric state that corresponds to the different input data streams, we train with a variation of the masked token model training objective from natural language processing that impels the network to learn the correlation between the different input streams and channels. To properly represent the statistical nature of the estimation of y given x, our network provides an ensemble prediction as a nonparametric model for the probability distribution over y.We present results for a network trained with a substantial amount of data, including different satellite observations (such as AMSU-A microwave sounders from NOAA 15-19 and the METOP satellites as well as IASI), radiosondes, and ground station-based measurements. The skill for both data assimilation and forecasting is analyzed and compared to ECMWF’s operational 4DVar system. We also ablate the effect different observations have on the skill of the network output.
Relating magma dynamics and ground deformation patterns at Mount Etna
Authors: McCluskey Owen , Papale Paolo , Montagna Chiara , Garg Deepak
Published: March 2024

We adopt a forward model approach to ground deformation through the use of GALES (GAlerkin LEast Squares) to simulate the mixing of magmas of variable compositions and temperatures, coupled with the elastodynamic response of the surrounding medium. GALES is a finite element parallel C++ code which solves for both the fluid and elasto-dynamic equations. Firstly, the mass, momentum and energy conservation equations are solved through GALES to simulate magma transfers across reservoirs connected through dykes. This allows the computation of the space-time stress distributions along the geometrically complex magma-rock boundary.Such stress distributions are then employed by GALES as a set boundary conditions in elasto-dynamic simulations to compute the space-time distribution of rock displacement within the heterogeneous rock system and on the free surface. The computed synthetic time series for surface ground deformation at places corresponding to the position of actual receiver stations are finally analysed and compared to the real observational data at Mount Etna. The geometry of the plumbing system, the temperature and major oxide composition of the involved magmas, and their water and carbon dioxide contents, are constrained from the bulk knowledge at the highly investigated, highly monitored, well-known Etna volcano. The space-time dependent physical properties of the multiphase magmas are computed on the basis of the local composition and volatile partition between the gas and melt phases.The results highlight the relationships between observations from surface monitoring networks and deep magma dynamics. In particular, they allow an in-depth investigation of ground oscillations with periods from seconds to hours, covering the intermediate range between seismic and geodetic observations which is being increasingly accessed to direct measurements. Such an intermediate frequency range emerges as being rich with new information on underground magma dynamics, potentially opening new perspectives and possibilities to magma monitoring and volcanic forecasts.

 
Effects of internal fracture variability on flow channelling in discrete fracture network models
Authors: Frampton Andrew
Published: March 2024

There is a need for improved understanding of flow channelling and solute transport in fractured crystalline rock in the context of safety analysis of geological repositories for spent nuclear fuel. Numerical discrete fracture network (DFN) models of sparsely fracture rock often employ an assumption of effectively homogeneous properties at the scale of individual fractures. However, real-world fractures have rough surfaces which translates to internal variability in aperture and permeability which can impact transport properties. Although it is known that internal variability controls flow channelling at the scale of single fractures, there is a lack of understanding of effects at the bedrock scale of multiple connected fractures forming networks. Therefore, it is relevant to study internal fracture variability in DFN models to better understand potential impacts on flow and transport.In this contribution, flow channelling and solute transport in three-dimensional fracture networks with internal variability in permeability is investigated using a numerical DFN flow model with a stochastic Lagrangian transport framework. The fracture network properties are obtained from field measurements and data of fractured rock from the Forsmark site in Sweden, which is a planned location for the construction of the spent nuclear fuel repository. Different assumptions for describing the correlation length of the textures used for internal variability of fracture permeability are considered. Multiple realisations are generated and it is shown that cases with strong correlation length can lead to reduced travel times and reduced solute retention when compared to cases assuming homogenous fractures. The changes observed occur only for a small fraction of early arrivals, whereas bulk mass is essentially unaltered, and late mass breakthrough with strong retention is generally enhanced. Cases with weak correlation lengths generally have minor impacts. Thereby key thresholds for cases where flow channelling is controlled by internal fracture variability versus network scale connectivity are discussed, and a need to understand and evaluate correlation structures for real-world fractures is highlighted.
Detecting signals of active tectonics with geomorphological methods in the slowly deforming Val di Fine Basin, Northern Apennines – limits to the resolution?
Authors: Kaerger Lauretta , Del Ventisette Chiara , Vannucchi Paola , Boswell Keir Derek , Pagli Carolina , Clementucci Romano , Molli Giancarlo
Published: March 2024

Tectonic and surface processes leave fingerprints on the modern topography. Deciphering tectonic signals becomes especially challenging when mechanisms at different wavelength overlap (e.g. faulting, deep-seated uplift). Nowadays, classic approaches can be combined with new tools and datasets to explore the morpho-structural evolution along a wide range of tectonic settings.The Val di Fine basin in the western side of the Tuscan Northern Apennines (Italy) is a slowly deforming area, generally assumed to be only of moderate to low seismic hazard. However, the 1846 ~M6 Orciano Pisano earthquake, responsible for significant destruction at the time, is strongly challenging this assumption. The event is presumed to have nucleated in the Val di Fine, however its source as well as precise location remain unclear and hence its seismological relevance.To clarify this question, we performed a new geomorphological analysis based on a 10x10 m DSM incorporating qualitative and quantitive methods (e.g. slope map, stream network analysis, knickpoint calculation). We complimented this work by field work, focusing on ground truthing of the geomorphic results and fault mapping, as well as a seismogenic approach relocating the freely available INGV earthquake catalogue.The results of the remote sensing analysis clearly show signs of several hundred meters uplift at the eastern side of the basin since the Pliocene as well as rather unspecific geomorphic features, raising new questions about the topographic development of the basin. The field data and seismogenic record clearly show signs of recent tectonic activity and uplift (newly mapped faults, seismites and small earthquake swarms) as well as clear indications that the basin likely features a more complex fault system then the N-S trending normal faults predominantly recorded in the region. However, these structures and possibly events seem to have left only very limited distinct detectable marks in the geomorphology. This decorrelation between the geomorphic results and field observations prompts the question why the tectono-geomorphic approach seems to be reaching its limit in this region.Factors like intense human activity and a dense vegetation in the area surely increase the noise level however these are common factors to be accounted for using remote sensing data. The cumulative results for this region rather point towards a complex morpho-structural evolution, characterized by a large-wavelength uplift component with local faulting activity. This may induce a complex response in the topographic expression leading to an overblending of the transient uplift signals through mid- and long-term developments making them more challenging to be detected with the current geomorphological methods.
First comparative exoplanetology between planets within the same system, and between young and mature sub-Neptunes
Authors: Barat Saugata , Desert Jean-Michel
Published: March 2024

We present results from the first comparative exoplanetology program between sister planets in the same young (20-30Myr old) system, V1298 Tau, and with more mature planets. A powerful combination of HST and JWST observations for this system provide the first constraints on atmospheric composition and elemental abundance ratios of two young transiting sub-Neptune/super-Earth progenitors with unpreceedented precision. This enables us to test the impact of stellar UV flux (orbital distance) on the atmospheric chemistry, haze properties by comparing their relative compositions and constrain late formation and early evolutionary scenarios using these constraints.  We reveal key differences between the nature and composition of the newborn sub-Neptunes/super-Earths with their mature counterparts, which we interpret as the first observational evidence for evolution of atmospheric composition, which is likely to have important implications for the overall thermal and atmospheric evolution of such planets. 
Large-scale drivers of the exceptionally low winter Antarctic Sea Ice Extent in 2023
Authors: Ionita-Scholz Monica
Published: March 2024

The year 2023 marked a turning point for the Antarctic region, as the Southern Hemisphere experienced a significant reduction in its sea ice cover, with a record-breaking sea ice minimum in July 2023 of ~2.4 million square kilometers below the long-term mean. This study investigates the drivers behind this exceptional event, by combining observational, satellite and reanalysis data. Throughout the year, the Antarctic Sea ice extent broke record after record, ranking as the lowest sea ice on record from January to September, with the exception of March and April. The exceptionally low sea ice extent from May to August was mainly driven by the prevalence of a zonal wave number 3 pattern, with alternating surface high- and low-pressure systems, which favored the advection of heat and moisture, especially over the Ross Sea (RS), Weddell Sea (WS), and Indian Ocean (IO). From May 2023 to August 2023, record-breaking low sea ice extent and high temperatures were recorded, and the most affected regions were RS, WS, and IO. Over the Weddell Sea, temperature anomalies of up to 10°C have been observed from May to July, whereas over the Ross Sea, temperature anomalies of up to 10°C have been observed, especially in July and August. A regime shift in the Antarctic Sea ice, as well as in the average mean air temperature and subsurface ocean temperature over the Weddell Sea, was detected around 2015. The analysis revealed complex interactions between atmospheric circulation patterns, oceanic processes, and their implications for variability and change in Antarctic Sea ice. Understanding the underlying mechanisms of these extreme events provides crucial insights into the changing dynamics of Antarctic Sea ice and its broader climatic significance.
Unintuitive patterns of change in global temperature distribution detected
Authors: Glynis Konstantinos , Iliopoulou Theano , Dimitriadis Panayiotis , Koutsoyiannis Demetris
Published: March 2024

Following the global warming observed in the past decades, interest in temperature’s variability and extremes has been rising, and has since become clear that it is imperative to identify the exact shape of the temperature’s distribution. Here, we analyze the temporal evolution of the near-surface air-temperature distribution in thousands of stations around the globe over the last centuries. Particularly, we explore a large set of daily timeseries (30–200 years) of average, maximum and minimum standardized temperature, and we apply robust high-order moments (K-moments) in both marginal and dependence structures, as an effective alternative to order statistics, and based on the analysis by Glynis et al. (2021). Results suggest that the average and minimum temperature tend to increase, while overall the maximum temperature is slightly decreasing. We subsequently characterize probabilistically the unintuitive patterns observed in the records.K. Glynis, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Stochastic investigation of daily air temperature extremes from a global ground station network, Stochastic Environmental Research & Risk Assessment, doi:10.1007/s00477-021-02002-3, 2021.
Linking Solar Flare Observations to a Series of Impulsive Solar Energetic Particle Events Measured with Solar Orbiter at 0.5 AU
Authors: Roco Mario , P. Janitzek Nils , Berger Lars , Kühl Patrick , Heidrich-Meisner Verena , Pacheco Daniel , Kollhoff Alexander , M. Mason Glenn , C. Ho George , Rodríguez-Pacheco Javier , Gómez-Herrero Raúl , Rodríguez-García Laura , Harra Luoise , Barczynski Krzysztof , P. Walsh Andrew , Zouganelis Yannis , Berghmans David , Krucker Sam , Francesco Battaglia Andrea , F. Wimmer-Schweingruber Robert
Published: March 2024

Impulsive solar energetic particle (SEP) events are typically associated with solar flares but the related particle injection and acceleration processes are still not well understood. We use in-situ and remote-sensing data from Solar Orbiter to establish a plausible link between a series of eruptions in a flaring region and a sequence of four SEP events measured at 0.5 AU between 5 and 6 March 2022. The direct comparison between these four events from the same source region allows to study the variability of the injected SEPs during an extended period of magnetic connectivity between Solar Orbiter and the flaring active region. In this study we analyze energetic electron, proton, and heavy ion data provided by the Energetic Particle Detector (EPD) suite onboard Solar Orbiter. Via a velocity dispersion analysis (VDA) of all measured particle species we estimate the solar event onset times which coincide with a series of solar eruptions that is observed by the Extreme Ultraviolet Imager (EUI) and the Spectrometer Telescope for Imaging X-rays (STIX) onboard Solar Orbiter. Further high-time-resolution EUV images and photospheric magnetic field information of the related active region is given by the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory. Solar Orbiter and Earth were nearly perfectly radially aligned at this time which enabled this additional remote sensing by SDO. We find that the energy spectra of the heavy ion in-situ measurements show significant differences between the four investigated SEP events in terms of overall particle intensity, spectral slope, and 3He / 4He abundances. By comparison with the remote-sensing observations we find that the two stronger SEP events (with higher 3He / 4He ratios) are related to solar eruptions with a more complex eruption pattern leading to extended brightening and restructuring of coronal loop structures. These new detailed observations can be used as starting point for quantitative modelling of flare-associated energetic particle acceleration and release in active regions.This work has been funded by the Spanish Ministerio de Ciencia, Innovación y Universidades project PID2019-104863RBI00/AEI/10.13039/501100011033.
Understanding geoscientific system behaviour from machine learning surrogates
Authors: Pomarol Moya Oriol , Karssenberg Derek , Immerzeel Walter , Nussbaum Madlene , Mehrkanoon Siamak
Published: March 2024

Machine learning (ML) models have become popular in the Earth Sciences for improving predictions based on observations. Beyond pure prediction, though, ML has a large potential to create surrogates that emulate complex numerical simulation models, considerably reducing run time, hence facilitating their analysis.The behaviour of eco-geomorphological systems is often examined using minimal models, simple equation-based expressions derived from expert knowledge. From them, one can identify complex system characteristics such as equilibria, tipping points, and transients. However, model formulation is largely subjective, thus disputable. Here, we propose an alternative approach where a ML surrogate of a high-fidelity numerical model is used instead, conserving suitability for analysis while incorporating the higher-order physics of its parent model. The complexities of developing such an ML surrogate for understanding the co-evolution of vegetation, hydrology, and geomorphology on a geological time scale are presented, highlighting the potential of this approach to capture novel, data-driven scientific insights.To obtain the surrogate, the ML models were trained on a data set simulating a coupled hydrological-vegetation-soil system. The rate of change of the two variables describing the system, soil depth and biomass, was used as output, taking their value at the previous time step and the pre-defined grazing pressure as inputs. Two popular ML methods, random forest (RF) and fully connected neural network (NN), were used. As proof of concept and to configure the model setup, we first trained the ML models on the output of the minimal model described in [1], comparing the ML responses at gridded inputs with the derivative values predicted by the minimal model. While RF required less tuning to achieve competitive results, a relative root mean squared error (rRMSE) of 5.8% and 0.04% for biomass and soil depth respectively, NN produced better-behaved outcome, reaching a rRMSE of 2.2% and 0.01%. Using the same setup, the ML surrogates were trained on a high-resolution numerical model describing the same system. The study of the response from this surrogate provided a more accurate description of the dynamics and equilibria of the hillslope ecosystem, depicting, for example, a much more complex process of hillslope desertification than captured by the minimal model.It is thus concluded that the use of ML models instead of expert-based minimal models may lead to considerably different findings, where ML models have the advantage that they directly rely on system functioning embedded in their parent numerical simulation model.
Exploring Groundwater Memory in Baltic States: Controls and Complexities of timeseries analysis
Authors: Bikše Jānis , Retike Inga , Haaf Ezra
Published: March 2024

This study used groundwater memory timeseries as a proxy for groundwater drought vulnerability to unveil dominant patterns and their correlations with physiographic and climatic controls within hydrogeological systems of Baltic states. Clustering of groundwater memory timeseries was performed to identify regions with similar hydrodynamic systems, while random forest classification identified significant site-descriptive features, giving insights into system similarities and dissimilarities. Catchment characteristics showed the greatest importance followed by climate, topography and land use features. Moreover, spatial analysis of cluster distribution revealed feature combinations which are not covered by groundwater observations, presenting a challenge in understanding groundwater dynamics in these data-scarce locations. The study underscores the importance of considering not only locations with groundwater level data but also regions of typical feature patterns without monitoring infrastructure, thus identifying possible locations for new groundwater monitoring wells. 
Multi-scale seismic imaging and related seismicity patterns of Krafla volcano and its geothermal system
Authors: Glück Elisabeth , Muzellec Titouan , Garambois Stephane , Vandemeulebrouck Jean , Ágústsdóttir Þorbjörg , Guðnason Egill Árni , Mortensen Anette
Published: March 2024

Krafla, one of the five central volcanoes of the Northern Volcanic Zone in NE-Iceland, last erupted during the Krafla Fires in the 70s and 80s. During the same period, a geothermal power plant was built within Krafla caldera, first operated in 1978. Both scientific and industrial interest led to an increase of knowledge of the complex system through systematic exploration with a wide variety of geophysical methods including seismic and electromagnetics coupled with borehole information.Among them, a local seismic network operated by Landsvirkjun and Iceland GeoSurvey, comprising 12 permanent broadband stations, has been continuously recording seismic data since 2013. We supplemented this network in June 2022 with a dense network of 98 nodes, resulting in two arrays, one large-scale, the other small-scale, operating in parallel.Here we present multi-scale 3-D velocity models for P-, S- and surface waves, independently derived for both networks through local earthquake and ambient noise tomographies. These models offer a glimpse into the subsurface structures of the volcanic system by utilizing various types of waves that are responsive to distinct rock/fluid properties and depths. The relocated and clustered seismic activity, documented by both permanent and temporary networks, underscores active structures pinpointed through tomography. With this we hope to strengthen the understanding of the connected volcanic and geothermal systems. Indeed, both the seismicity and strong velocity anomalies are located at similar depths as the magma batch that was drilled into with the IDDP1.
Boundary-layer cloud modeling challenges on the North Slope of Alaska
Authors: Fitch Kyle , Cleveland Zachary , Stanford McKenna , Dedrickson Lindsay
Published: March 2024

The accurate modeling and prediction of cloud base heights is critical for energy balance calculations and aviation operations, alike. Low-level (i.e., boundary-layer) Arctic clouds can be difficult to model, making prediction of formation and dissipation challenging. Primarily mixed-phase, these clouds typically contain low quantities of supercooled liquid water and often slowly precipitate relatively small amounts of moderately and heavily rimed snow particles. While this appears to be the predominant cloudy state on the North Slope of Alaska (NSA), the delicate balance of microphysical, dynamical, radiative, surface coupling, and advective processes can rapidly shift to heavy snow (with various degrees of riming) or to a complete dissipation of the cloud layer without any precipitation, depending on the dominant processes. Here we strive to disentangle these various processes. First, we compare the predictive performances of four different numerical weather models in forecasting the presence and base-heights of low-level clouds: the High-Resolution Rapid Refresh - Alaska (HRRR-AK) model, the Polar Weather Research and Forecasting (Polar WRF) model, the Unified Model (UM), and the European Centre for Medium-range Weather Forecasting (ECMWF) model.  Initial results comparing model output at two U.S. Department of Energy Atmospheric Radiation Measurement (AMT) NSA sites, during the fall season in 2019 and 2022, show that the UM slightly outperforms the HRRR-AK in terms of accurately forecasting the presence of a low-level cloud layer (89% of the time). All models have a significant bias of 300 to 800 meters in forecasting cloud base height (lower than is observed); however, the UM and ECMWF models have the lowest biases. Finally, a case study for a particularly challenging April 2017 thin-cloud event is presented, wherein we compare the performance of four different bulk microphysical parameterization schemes using a higher-resolution large eddy simulation (LES) model, the WRF-LES. Initial results show that the Thompson scheme was the only one able to reproduce and sustain a substantial supercooled liquid layer, but it was unable to reproduce the transition from a deep, liquid-rich cloud to a thin layer with moderately and heavily rimed precipitation. This is the first step in linking simulated LES-scale riming processes with those parameterized at a coarser mesoscale model scale. This has important implications for forecasting low-level clouds in an operational environment, given the efficiency of the riming process.