bluuebunny/crossref_metadata_2025 huggingface dataset:
Simulation Modeling of Arid Grassland Dynamics in Jordan: A 40-Year Analysis of Leaf Area Index Trends
Authors: Alhamad Mohammad Noor , Abdullah Shefaa
Published: March 2024

This study employs the PHYGROW simulation model to assess the 40-year dynamics of arid grassland in Jordan, focusing on the Leaf Area Index (LAI) as a pivotal indicator of vegetation health. The observed results reveal a notable decline in LAI over the study period, with the highest recorded value in 2005 (2.27) and a subsequent reduction to 1.68 in 2021. Rigorous statistical analyses, including regression analysis, confirm the significance of this downward trend, prompting further investigation into potential contributing factors such as changes in climate, land use, and soil conditions. Interannual variability analysis identifies specific years marked by noteworthy LAI fluctuations, providing insights into the dynamic responses of the arid grassland ecosystem. Comparison with concurrent climate data underscores the intricate relationship between LAI trends and environmental variables. The study emphasizes the importance of continuous monitoring and understanding the underlying drivers of vegetation dynamics in arid regions.The observed decrease in LAI holds implications for the overall health and resilience of the ecosystem, highlighting the need for informed decision-making in sustainable land management practices. These findings contribute significantly to the broader understanding of arid land dynamics, guiding future research and collaborative efforts with experts in related fields. Such collaborations are essential for enhancing the robustness and applicability of the results, ultimately informing conservation and resource management strategies tailored to the unique challenges of arid environments.
Decadal trends of global climate and air pollution: two-way interactions, joint impacts and synergistic mitigation
Authors: Xu Yangyang
Published: March 2024

Climate change and air pollution are arguably two most pressing environmental issues facing the world today. This talk gives an overview of recent studies relating the two at the decadal to multi-decadal time scale. The first part focusing on interactions will review recent works on how global warming affects aerosol distribution (Banks et al., 2021; Fiore et al., 2022), and in turn, how aerosols affect mean and extreme precipitation (Xu et al., 2022) and circulation changes (Diao and Xu, 2022), which could consequently impact air pollution itself (Wang et al., 2021), completing an intrinsic two-way feedback loop.The second part will address the joint occurrence of heat extremes and air pollution, including haze (Xu et al., 2020) and ozone (Xiao et al., 2022), raising awareness of their broader impact on human health and crop yield. Some concluding thoughts are given on how to mitigate the near-term warming rates by achieving co-benefits of air quality improvement (Ocko et al., 2021), while avoiding the temporary shock of aerosol unmasking (Dreyfus et al., 2022). A novel yet simple integrated human-Earth modeling framework is introduced to further demonstrate the importance of cutting non-CO2 pollutants to stabilize global warming (Xu and Ramanathan, in review).
Robust predictions of changes in evenness of global precipitation under global warming
Authors: Hsu Hsin , Fueglistaler Stephan
Published: March 2024

Global mean precipitation is anticipated to increase by 2-4% per degree Kelvin, with intense events scaling at 7%, driven by boundary layer humidity. The understanding of the change in daily-to-annual precipitation probability density function remains rather incomplete. To address this knowledge gap, we employ Gini index to evaluate spatial unevenness and temporal inequality of precipitation under global warming in CMIP6 models. We observe heightened spatial unevenness of daily precipitation in tropics and extratropics over land and ocean. While the tropics maintain this unevenness over time, indicating large-scale convection aggregation, extratropical precipitation evens out with increasing timescales. This disparity suggests distinct processes governing daily and annual mean precipitation, underscoring the intensification of stronger storms over weaker events. Globally, temporal inequality is on the rise, with more pronounced intensification in regions where projected precipitation deviates significantly from Clausius–Clapeyron scaling. Our hypothesis posits that the shift in precipitation distribution under warming projections stems from an increase in no-rain days coupled with rainfall events scaled by a constant. To assess this proposition, we construct a toy model predicting projected temporal inequality based on local hydroclimate conditions pre-warming, the projected mean precipitation, and a theorem-derived stretching parameter. The toy model demonstrates robust performance overall, except in regions notably influenced by the Hadley cell. Additionally, the model suggests that local precipitation events are scaled by a constant of approximately 1.07. Our analysis establishes meaningful connections among changes in mean precipitation, precipitation distribution, and dry-day number, offering comprehensive insights into hydroclimate transformations under global warming.
The Unreasonable Efficiency of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation
Authors: Hwong Yi-Ling , Muller Caroline
Published: March 2024

The elimination of rain evaporation in the planetary boundary layer (PBL) has been found to lead to convective self-aggregation (CSA) even without radiative feedback (frequently referred to as “moisture memory aggregation”), but the precise mechanisms underlying this phenomenon remain unclear. We conducted cloud-resolving simulations with two domain sizes (L = 128 and 256 km; Δx = 1 and 4 km) with homogenised radiation and progressively reduced rain evaporation in the PBL by multiplying it with a factor 𝛼 = [1.0, 0.8, 0.6, 0.4, 0.2, 0]. Surprisingly, self aggregation only occurred when rain evaporation was almost completely removed (𝛼 ≈ 0). Similar to conventional radiatively-driven aggregation (RDA), a shallow circulation that leads to an upgradient moist static energy transport is present, but in this case it is the additional convective heating resulting from the reduction of evaporative cooling in the moist patch that triggers this circulation, thereafter a dry subsidence intrusion into the PBL in the dry patch takes over and intensifies aggregation. Hence, this type of aggregation should be more appropriately referred to as “convectively-driven aggregation” (CDA). Contrary to RDA, in CDA temperature and moisture anomalies oppose each other in their buoyancy effects, hence explaining the need for near-zero 𝛼 values: only when rain evaporation is almost completely removed can the additional heating trigger aggregation. Lastly, we found radiative cooling and not cold pools to be the leading cause of the domain size dependence of CDA. Runs with similar amounts of cold pools aggregate in the large but not small domain due to stronger radiative cooling rates and concomitant broadening of the range of precipitable water in the larger domain. 
Subsurface characterization using Distributed Acoustic Sensing (DAS) on an offshore fiber between Denmark and Norway
Authors: Damsgård Jonas , Hansen Thomas , Voss Peter , Hansen Henrik , Steffansen Simon , Nørmark Egon , Fyhn Michael
Published: March 2024

In April 2023 a seismic survey was carried out in southern Skagerrak using a towed-streamer and airgun setup. The aim of the survey was investigating the suitability of the Jammerbugt structure for CO2 storage. A fiber-optic cable is co-located with the Skagerrak 4 high-voltage interconnector cable between Denmark and Norway. The fiber was crossed multiple times by the surveying ship. Relative strain was measured along a 80 km section of the fiber using Distributed Acoustic Sensing (DAS) during the active seismic survey. Seismic arrivals from the airgun shots were clearly recorded by the fiber. The DAS data also contains a large number of other signals caused by passing ships and wave interactions. Shot-gathers were extracted from the DAS data using the timing and location of airgun shots. These were subsequently processed and compared with traditional shot-gathers recorded by the towed-streamer. The DAS data contains distinguishable direct, refracted and surface wave arrivals from the airgun shots. Reflection hyperbolas are also observed in the DAS data at larger receiver-offsets, but only when the source is close to the fiber. The comparison indicates that DAS is able to at least partially record the same wavefield from an active source as that recorded by hydrophones. Consequently the DAS data can be used for imaging and subsurface characterization.The utilized DAS interrogator unit is owned by the danish transmission system operator, Energinet, who provided the DAS data for this study. Data processing is carried out using MatLab and Promax.
Integrating Deterministic and Probabilistic Approaches for Improved Hydrological Predictions: Insights from Multi-model Assessment in the Great Lakes Watersheds
Authors: Romero-Cuellar Jonathan , Arabzadeh Rezgar , Craig James , Tolson Bryan , Mai Juliane
Published: March 2024

The utilization of probabilistic streamflow predictions holds considerable value in the domains of predictive uncertainty estimation, hydrologic risk management, and decision support in water resources. Typically, the quantification of predictive uncertainty is formulated and evaluated using a solitary hydrological model, posing challenges in extrapolating findings to diverse model configurations. To address this limitation, this study examines variations in the performance ranking of various streamflow models through the application of a residual error model post-processing approach across multiple basins and models. The assessment encompasses 141 basins within the Great Lakes watershed, spanning the USA and Canada, and involves the evaluation of 13 diverse streamflow models using deterministic and probabilistic performance metrics. This investigation scrutinizes the interdependence between the quality of probabilistic streamflow estimation and the underlying model quality. The results underscore that the selection of a streamflow model significantly influences the robustness of probabilistic predictions. Notably, transitioning from deterministic to probabilistic predictions, facilitated by a post-processing approach, maintains the performance ranking consistency for the best and worst deterministic models. However, models of intermediate rank in deterministic evaluation exhibit inconsistent rankings when evaluated in probabilistic mode. Furthermore, the study reveals that post-processing residual errors of long short-term memory (LSTM) network models consistently outperform other models in both deterministic and probabilistic metrics. This research emphasizes the importance of integrating deterministic streamflow model predictions with residual error models to enhance the quality and utility of hydrological predictions. It elucidates the extent to which the efficacy of probabilistic predictions is contingent upon the sound performance of the underlying model and its potential to compensate for deficiencies in model performance. Ultimately, these findings underscore the significance of combining deterministic and probabilistic approaches for improving hydrological predictions, quantifying uncertainty, and supporting decision-making in operational water management.
Anvil cloud thinning in high-resolution models implies greater climate sensitivity
Authors: Sokol Adam , Wall Casey , Hartmann Dennis
Published: March 2024

Anvil clouds produced by tropical convection are expected to shrink in area as the climate warms, and the associated radiative feedback has long been the subject of controversy. In the World Climate Research Programme’s (WCRP) recent assessment of equilibrium climate sensitivity (ECS), the anvil area feedback was the least certain of any individual feedback process but was nevertheless estimated to be significantly negative. Here we show that such a negative feedback is not supported by an ensemble of high-resolution atmospheric models. On the contrary, the models suggest that changes in high cloud area and opacity act as a modest positive feedback. The positive opacity component arises from the disproportionate reduction in the area of thick, climate-cooling anvils relative to thin, climate-warming clouds. This suggests that thick cloud area is tightly coupled to the rate of convective overturning—which is expected to slow with warming—whereas thin cloud area is influenced by other processes. The cloud response is examined from a novel perspective that treats high ice clouds as part of an optical continuum as opposed to entities with fixed opacity. The positive feedback differs significantly from previous estimates and leads to a 0.3 °C increase in the WCRP estimate of ECS and a 10% widening of the likely range. We find that constraining the response of thin, high clouds in the Tropics to warming is critical for improved estimates of cloud feedback and global change.
Clustering distributed acoustic sensing signals via curvelet transform and unsupervised deep learning
Authors: Li Bolin , de Ridder Sjoerd , Nowacki Andy
Published: March 2024

Distributed acoustic sensing (DAS), a technology that exhibits great potential for subsurface monitoring and imaging, has been regarded as a preeminent instrument for vibration measurements. In light of the tremendous amount of seismic data, numerous channels, and elevated noise levels, it becomes imperative to suggest an appropriate denoise procedure that is compatible with DAS data. In this regard, unsupervised deep learning with data clustering generally exhibits superior performance in facilitating the efficient analysis of sizable unlabeled data sets devoid of human bias. In addition, the clustering method is capable of detecting seismic waves, microseismic turbulence, and even unidentified new types of negligible seismic events, in contrast to a number of conventional denoising techniques. While current approaches reliant on f-k analysis remain valuable, they fail to fully exploit the information present in the wavefield due to their inability to identify the characteristic moveout observed in seismic data. In order to denoise DAS data more effectively, we investigate the capacity of the curvelet transform to extend existing deep scattering network methodologies. In this paper, we propose a novel clustering approach for the denoise processing of DAS data that utilises the Gaussian Mixture Model (GMM), curvelet transform, and unsupervised deep learning. The DAS data are initially subjected to the curvelet transform in order to derive the curvelet coefficients at various scales and orientations, which can be regarded as the first layer of extracted features. Following this, a deeper layer of features is obtained by applying the curvelet transform to the coefficients in the first layer. The aforementioned process continues in this manner until the depth of the layer satisfies the algorithm-determined expectation. By concatenating the curvelet coefficients from each layer, the original DAS data's features are generated. Afterwards, the signal is reduced to two dimensions using principal component analysis (PCA), which simplifies its interpretation by projecting the high-dimensional features onto two principal components, which facilitates the clustering of the features by GMM for achieving the final clustered results.This methodology operates without the need for labels of DAS data and is highly appropriate for managing the substantial quantity and numerous channels of DAS. We used a variety of approaches, such as Bayesian information criteria and silhouette analysis, to determine the optimal number of clusters in GMM and evaluate the algorithm's clustering performance. We demonstrate the method on downhole data acquired during stimulation of the Utah FORGE enhanced geothermal system, and the results appear quite satisfactory, indicating that it can be utilised effectively to denoise DAS signals.
Potential Climate Change Mitigation and Environmental Impacts from the Widespread Implementation of Ocean Thermal Energy Conversion 
Authors: Nickoloff Anna , Olim Sophia , Eby Michael , Weaver Andrew
Published: March 2024

Ocean thermal energy conversion (OTEC) is a form of renewable energy that could potentially displace a significant amount of fossil-fuel generated electricity. Many multi-century simulations of the UVic Earth Systems Climate Model (UVic ESCM) are presented to better understand the climate change mitigation potential and the projected magnitude and significance of the impacts of widespread OTEC implementation at varying total power outputs (3, 5, 7, 10, and 15 TW). This study builds on previous research with the inclusion of a fully coupled atmospheric model, sea ice model, and comprehensive carbon cycle model. In high emission scenarios (Representative Concentration Pathway 8.5), OTEC was found to be able to briefly produce over 36 TW of power and power production rates of 6 TW and below were found to be sustainable on multi-millennial timescales. The study also included an emission reduction associated with OTEC that resulted in cumulative emission reductions of 1190-3600 Pg C by 2500 relative to a control scenario without OTEC deployment. Environmental impacts include globally averaged sea surface temperature decreases of 0.8-3ºC relative to control values, increased heat uptake at intermediate depths, and enhanced biological production. The implementation of OTEC was found to induce overturning cells in the North Pacific and cause significant relative increases in strength of the maximum Meridional Overturning Circulation globally with values ranging from 1.6 to 8.2 Sv by 2500, depending on the level of OTEC power generation. While caution is required and the engineering challenges would be large, early indications suggest that the large-scale implementation of OTEC could make a substantial contribution to climate change mitigation.
Automated analysis of strain-stress curves for aquifer system characterization 
Authors: Navarro-Hernández María , Pozo Sergio , Valdes-Abellan Javier , Tomás Roberto
Published: March 2024

Excessive groundwater extraction, often leads to changes in aquifer-system layers, causing  anthropogenic-triggered land subsidence. The reduction in pore pressure due to groundwater withdrawal acts as an external factor, while soil compressibility serves as the primary internal factor of land subsidence. The interaction between stress and strain within an aquifer-system, or specific layers, is typically represented through stress-strain curves. These curves, illustrating the hydrograph data (stress induced by piezometric level variations) against land subsidence compaction records (strain), offer valuable insights into the geomechanical behaviour of the aquifer-system, such as elastic, plastic, elasto-plastic, or visco-elasto-plastic behaviour. Additionally, these curves can be employed to estimate hydrogeological parameters like the storage coefficients of the aquifer-system. Traditionally, determining storage coefficients from stress-strain curves has relied on subjective visual assessments by skilled researchers. In this study, we proposed a MATLAB© application designed to automate and streamline the analysis of land subsidence datasets. The application facilitates the exploration of potential correlations with piezometric levels and allows for the estimation of storage coefficients. This approach reduces the time-cost of analysis and minimizes potential human-interpretation errors. The developed application integrates temporal series of groundwater levels from observation wells and ground deformation measurements o automatically generate stress-strain curves. To illustrate and validate the effectiveness of the proposed application, the proposed app is applied to diverse aquifer-systems worldwide, each exhibiting distinct geomechanical behaviour. The results showcase the tool's capability in efficiently studying and understanding land subsidence, providing a valuable resource for scientists and researchers investigating the impacts of excessive groundwater extraction on land deformation. 
Replacing Catchment Timescale (Tc) in the rainfall-runoff model based on the linear reservoir equation with Catchment Response Time Obtained via Detrending Moving Cross-Correlation Analysis (DMCA) Method 
Authors: Yin Yiming , Woods Ross , Rosolem Rafael
Published: March 2024

The accurate estimation of catchment response time is crucial for understanding hydrological processes and making informed water resource management decisions. In the rainfall-runoff model with linear reservoir equation, the time parameter, Tc, represents the time delay of catchment routing. Recent studies have shown that the Detrending Moving Cross-Correlation Analysis (DMCA) method can provide a more practical and direct determination of catchment response time, surpassing the limitations of the linear reservoir model.This study aims to investigate whether the catchment response time obtained through the DMCA method can be effectively utilized as a replacement for the catchment timescale in the rainfall-runoff model with linear reservoir equation. The DMCA method is employed to analyze hydrological time series data from the CAMELS GB (Catchment Attributes and Meteorology for Large-sample Studies in Great Britain) hourly time series data. The calculated catchment response times are then compared with the catchment timescales estimated using the linear reservoir equation.Our results indicate that the catchment response time derived from the DMCA method exhibits a closer correspondence with the catchment timescale estimated by the linear reservoir equation.In conclusion, this study demonstrates the potential of replacing the catchment timescales in the rainfall-runoff model with linear reservoir equation with catchment response times obtained through the DMCA method. This substitution leads to more robust and accurate hydrological modeling, particularly in ungauged catchments or catchments with limited timeseries data.. The research findings indicate that the DMCA method offers a valuable tool for hydrologists and water resource managers seeking to enhance their understanding and management of catchment dynamics. However, further research is warranted to explore its application across catchments from different regions.
The frontiers of distributed acoustic sensing for seismological applications
Authors: Biondi Ettore , Li Jiaxuan , Saunders Jessie , Husker Allen , Zhan Zhongwen
Published: March 2024

Distributed acoustic sensing (DAS) is proving to be an effective technology for seismological applications. Its success is due to the ability to deploy DAS instrumentation on the existing ever-growing telecommunication fiber networks across the globe. However, the benefits of DAS are hindered by the sheer volume of data commonly recorded from single-instrument deployments, which can easily reach tens of TBs. Additionally, since DAS measures along fiber strain, new data analysis paradigms are necessary to exhaustively exploit all the information contained within these large datasets. We showcase successful applications of DAS experiments using existing fiber cables located in different scenarios, from volcanic systems to densely populated urban environments. To harness the information within these novel datasets, we combine machine-learning tools with efficient algorithms running on high-performance computing architectures. For example, we showcase how the arrival times obtained from PhaseNet-DAS can provide real-time earthquake detection and localization, allowing for the inclusion of DAS data within earthquake early warning systems. Moreover, we demonstrate the capability of integrating real-time streamed DAS channels within seismic network operations. Our processing paradigm is proving to be an effective ground for discoveries and for creating the next generation of seismic monitoring frameworks.
Subduction initiation (or not) due to absolute plate motion at STEP faults: The New Hebrides vs. the Tonga examples
Authors: Martinez Fernando
Published: March 2024

Subduction initiation remains one of the least understood global processes of plate tectonics.  Prominent models have been cast in terms of two broad classes: “spontaneous” cases due to lithospheric gravitational instabilities and “induced” cases due to forced plate convergence. Yet gravitationally unstable lithosphere is old, strong, and difficult to begin to bend into a subduction zone and convergent forces necessary to begin subduction are often too large given the plates involved. These models also consider the asthenospheric mantle as passive, even though relative motion between slabs and the asthenosphere has long been regarded as a strong control on subduction dynamics. Here I propose that subduction-transform edge propagator (STEP) faults can initiate subduction depending on the absolute motion of the STEP fault with respect to the asthenosphere. STEP faults form where subduction zones end and the subducting plate tears forming a down flexed transcurrent plate boundary at the surface shearing against the adjacent rear arc lithospheric plate. However, STEP faults are not simple transcurrent boundaries. Absolute motion of the down flexed STEP fault edge with respect to the surrounding asthenosphere can produce a strong “sea anchor” force that either continues to bend the edge downward, initiating subduction, or opposes slab bending, inhibiting subduction. In the south Pacific, the southern end of the New Hebrides Trench and the northern end of the Tonga Trench are type-example STEP faults with opposite senses of dip but both moving northward with respect to the asthenosphere. The northward dipping New Hebrides STEP fault moves northward in a mantle reference frame creating a strong asthenospheric flow against the STEP fault edge, inducing active subduction at the Matthew-Hunter trench. In contrast, the Tonga STEP fault dips southward but also has a northward component of motion with respect to the mantle. Asthenosphere thus flows southward beneath the down flexed Tonga STEP fault edge opposing further bending.  Subduction does not initiate at the Tonga STEP fault despite a ~100 Myr age contrast between the Pacific and north Fiji and Lau basin lithospheres. Since absolute plate motions reflect the sum of all forces acting on the entire lithospheric plate, a strong sea anchor mantle force may be generated at a STEP fault edge, initiating subduction (or inhibiting it), even where lithosphere is old, strong, and resists bending and without requiring large convergent forces between plates, overcoming these objections to previous models.
Subseasonal Typhoon Precipitation Forecast in Taiwan Area Using the ECMWF Reforecasts: Forecast Verification and Application
Authors: Hsu Han-Yu , Tsai Hsiao-Chung
Published: March 2024

The main objective of this study is to assess typhoon precipitation forecast skill on the subseasonal timescale. The 20-year reforecasts from the ECMWF 46-day ensemble (ENS) are utilized to compare with gridded surface observations in Taiwan. The analysis focuses on the dates when typhoons affect Taiwan (117-129°E and 19-28°N). 15 ENS grids around Taiwan area are used with the grid size of 0.8 x 0.8 degree. Historical rainfall observations are provided by the Central Weather Administration (CWA), which the observations from the surface stations are interpolated into a resolution of 1km x 1km grid box. A comparison between the ENS forecast data and gridded CWA rainfall observations is performed by searching the optimal percentile rank (PR) of gridded CWA rainfall that has the smallest mean difference against the ENS data. The result reveals that the ENS can somewhat capture the rainfall contrast between the mountainous area and plain area, despite its relatively lower horizontal resolution. However, the difference between ENS rainfall forecasts and surface observations significantly increases for the forecasts beyond 72 hours, due to the model's coarser resolution and typhoon track forecast errors.The ENS typhoon track forecast errors in weeks 1-4 are analyzed by comparing the ensemble vortex tracks with the JTWC best tracks. The track forecast error is decomposed into the along-track (AT) and cross-track (CT) components. The analysis result shows negative mean AT errors, indicating slower translation speed biases in the model. The mean AT errors could reach up to 400 km for the 168 h forecasts after TC formations.Given the significant typhoon track forecast errors, using the raw ENS rainfall forecasts for the operational TC forecasting/outlook become challenging. In response, we have developed a statistical Quantitative Precipitation Forecast (QPF) model to predict typhoon rainfall, considering the track biases in the ENS forecasts. The forecast tools developed in this study will be integrated into CWA’s subseasonal typhoon forecast system to support water resources management and disaster risk reduction.
Uncertain Changes of Vegetation Greenness Interannual Variability on Half of the Global Vegetated Surface
Authors: Tian Jiaqi , Luo Xiangzhong
Published: March 2024

Changes in the interannual variability (IAV) of vegetation greenness and carbon sequestration are key indicators of the stability and climate sensitivities of terrestrial ecosystems. Recent studies have examined the changes in the vegetation IAV using atmospheric CO2 observations and dynamic global vegetation models (DGVMs), however, reported different and even contradictory trends of IAV. Here, we investigate the changes in the IAV of vegetation greenness, quantified as coefficient of variability (CV), over the past few decades based on six long-term and three short-term satellite remote sensing products. Our results suggested that on half of the global vegetated surface, CV trends were uncertain (i.e., inconsistent CV trends when using different satellite remote sensing products). Meanwhile, we found that 22.20% and 28.20% of the global vegetated surface (i.e., mostly in the non-tropical land surface) show significant positive and negative CV trends (p ≤ 0.1), respectively. Regions with higher air temperature and greater aridity tend to have increasing CV trends, whereas greater vegetation greening trend and higher nitrogen deposition lead to smaller CV trends. Our study provides a remote sensing-based examination of the changes in the IAV of global vegetation greenness, and highlights the potential issues in studying the response of terrestrial ecosystems to climate change.
Detection of long-lasting aurora-like radio emission above a sunspot
Authors: Yu Sijie , Chen Bin , Sharma Rohit , Bastian Timothy , Mondal Surajit , Gary Dale , Luo Yingjie , Battaglia Marina
Published: March 2024

We present our findings on long-lasting radio emissions above a sunspot, analogous to planetary auroral radio emissions. These emissions are typically characterized by highly polarized, intense radio bursts, generally attributed to electron cyclotron maser (ECM) emission from energetic electrons in regions with converging magnetic fields, such as planetary polar areas. Similar bursts have been observed in magnetically active low-mass stars and brown dwarfs, often prompting analogous interpretations. Here we report observations of long-lasting solar radio bursts with high brightness temperature, wide bandwidth, and high circular polarization fraction akin to these auroral and exo-auroral radio emissions, albeit two to three orders of magnitude weaker than those on certain low-mass stars. Our spatial, spectral, and temporal analysis indicate that the source is situated above a sunspot where a strong, converging magnetic field is present. The morphology and frequency dispersion of the source align with ECM emissions, likely driven by energetic electrons from recurring nearby solar flares. These observations provide new insights into the nature of intense solar radio bursts and suggest a potential model for understanding aurora-like radio emissions in other flare stars with significant starspots.
Observational Analysis of In Situ Ring Current Density: A Multi-Mission, Multi-Perspective, and Multi-Spacecraft Approach
Authors: Tan Xin , Dunlop Malcolm , Yang Yanyan , Yang Junying , Russell Christopher
Published: March 2024

We systematically compare and analyze diverse methodologies for calculating space current density, with a particular focus on examining the insights and challenges associated with the Curlometer method. Employing these methodologies, we conduct an in-depth analysis of an event characterized by elevated calculated current densities, delving into its physical authenticity. Statistical analysis of the long-term measurements of particle and magnetic field values from multiple missions is also employed to investigate the distribution characteristics of current density within the ring current region. Despite the inherent limitations in the available data coverage, which preclude a comprehensive revelation of the spatial topology of the entire ring current, our preliminary findings indicate a season-dependent warping structure in the ring current, in both latitude and local time. This structural variation is fundamentally attributed to the combined influence of the Earth dipole field tilt and solar wind. These results are anticipated to contribute to the understanding of magnetospheric current systems, particularly elucidating the coupling mechanisms between the ring current and other current systems.
The Impact and Mechanism of Magnetic Fields on Plasma Dynamics in the Martian Space Environment
Authors: Li Shibang , Lu Haoyu , Cao Jinbin , Cui Jun , Chen Nihan , Song Yihui , Wang Jianxuan
Published: March 2024

The absence of a global magnetic field at Mars results in a direct interaction between the solar wind and the ionosphere, leading to ion escape from its atmosphere to space. However, the existence and asymmetric distribution of crustal fields introduce significant complexity into the plasma dynamics within the Martian environment, resulting from a disordered magnetic field topology characterized by its orientation parallel, directed towards, and away from the Martian surface. Based on three-dimensional multifluid magnetohydrodynamic simulations, we investigated the impact of the magnetic inclination angle on the Martian ionospheric plasma dynamics under the typical solar wind conditions. Numerical results showed that ions can be effectively diffuse upwards along vertical magnetic fields driven by the electron pressure gradient and the motional electric force, leading to a strong outward flux escaped through plume and the magnetotail eventually. In addition, due to the Hall electric force, there is a tendency for ion flow to be deflected in the horizontal plane. These results provide valuable insights into the influence of magnetic fields on ion motion in the Martian space environment.
A Machine-learning-based Model of the Three-dimensional Ion Flux in the Earth’s Northern Cusp.
Authors: Cucho-Padin Gonzalo , Sibeck David , Wang Xueyi
Published: March 2024

Magnetic reconnection on the dayside magnetopause is considered the primary mechanism for transporting mass, momentum, and energy from the solar wind into the terrestrial magnetosphere. Several studies have demonstrated that the spatiotemporal dynamics of the dayside magnetic reconnection can be inferred remotely from the analysis of the time-energy dispersion of ions in the Earth’s cusps. Despite the immense number of in-situ cusp measurements acquired by numerous space-based instruments, it is still challenging to determine the overall cusp behavior owing to the intermittency of the measurement acquisition. To overcome this issue, this work implements a regression model of the three-dimensional (3-D) ion flux in the Earth’s Northern cusp based on artificial neural networks (ANN) and numerous of measurements of the cusp under varying solar wind conditions. For training process, we have used solar wind parameters and geomagnetic indices obtained from the OmniWeb database as input, and in-situ ion flux measurements acquired by the CIS/CODIF/HIA instruments on board ESA’s multi-spacecraft Cluster mission during the period from 2001 to 2009 for supervised output. The model allows the reconstruction of the time-dependent, 3-D ion flux distribution within the cusp region, which serves to determine the boundaries of the high-altitude cusp, analyze its structural response to time-dependent solar wind conditions, and investigate the relationship between the cusp and dayside magnetic reconnection. In addition, we will show how an explainability technique (DeepSHAP) on the ANN-based model can be used to investigate the physical relationship (importance) of each of the inputs with the reconstructed ion flux structure.
Hydrogeobiochemical Modelling for Bioremediation of Mono-Aromatic Hydrocarbons Using Nitrate-Sulfate-Reducing Assemblages in Aquifers
Authors: Srivastava Akanksha , Valsala Renu , Jagadevan Sheeja
Published: March 2024

Contamination with mono-aromatic hydrocarbons, specifically benzene, toluene, and xylenes (BTX), is one of the major concern to groundwater aquifers. BTX have high environmental stability and are harmful to human health and aquifer ecosystem. Thorough assessment and monitoring of the risk posed by BTX in aquifers are essential for the sustainable use of groundwater resources. The biodegradation of BTX in aquifer rely primarily on anaerobic processes. Nitrate-sulfate-reducing assemblages is considered for BTX bioremediation in such anoxic condition. These assemblages act as a terminal electron acceptor for bacterial respiration. The degree of the interaction between combinations of nitrate-sulfate reduction and BTX elimination determines the efficacy of BTX biological degradation. The interactions, however, received limited attention in the existing literature. Hence, the current analysis focuses co-existence of nitrate-sulfate assemblages affecting BTX bioremediation. A multi-component numerical simulation is performed to investigate the potential of nitrate-sulfate-assemblages for bioremediation of BTX in anoxic conditions. A fully implicit finite-difference novel approach is adopted here to solve the proposed numerical model, which is capable of obtaining spatial variation in BTX concentrations. The results suggest that bioremediation is efficient in removing toxic BTX from aquifers under the coexistence of nitrate-sulfate assemblages. This approach, in addition, can be used in deciding the optimum rate of electron acceptor injection and the time required to bring BTX to standard limits. Furthermore, it can help us to plan sustainable bioremediation strategies for mono-aromatic hydrocarbon contaminated aquifers where such reduction assemblages co-exist. This hydrogeobiochemical modelling study also emphasizes the importance of multidisciplinary methods in dealing with challenging environmental issues in the contaminated aquifers.Keywords: Hydrogeobiochemical modelling; Bioremediation; BTX; Nitrate-sulfate assemblages; Aquifers.
Contrasting fates of particulate and mineral-associated organic carbon under afforestation potentially obscure soil carbon accumulation
Authors: Cheng Xiaoli , Zhai Deping
Published: March 2024

Afforestation is widely believed to sequester carbon (C) in soil. However, the effect of afforestation on soil organic carbon (SOC) accumulation is still debated due to contrasting features of particulate and mineral-associated organic carbon (POC and MAOC). We conducted a field investigation of 144 paired sampling sites by comparing afforested and non-afforested lands to investigate the POC and MAOC dynamics after afforestation at a regional scale in central China. The average SOC, POC and MAOC levels were significantly increased by afforestation; however, POC and MAOC responded differently to afforestation type. With increasing SOC content, the POC content increased consistently, while the MAOC content tended toward saturation. Afforestation hindered the POC saturation deficit potential but increased the MAOC saturation capacity. Thus, the different responses of POC and MAOC to afforestation obscured the distinct patterns of C accumulation triggered by afforestation. We detected the dominant predictors of variations in POC shifted from soil properties to microbial traits following afforestation, and afforestation increased the relative contribution of microbial traits to MAOC variation, making microbe as the leading predictor. The divergent fates of POC and MAOC and importance of microbe-related processes advance understanding of soil C cycling under afforestation with respect to C neutrality.
Larger Benthic Foraminifera across the Cretaceous/Paleogene boundary and their implications for the India–Eurasia collision: Insights from Baluchistan Basin, Pakistan (Eastern Neo-Tethys)
Authors: Muhammad Kamran , Dangpeng Xi , Fabrizio Frontalini , Kamran Mirza , Tian Jiang , Ghulam Akbar , Xiaoqiao Wan
Published: March 2024

The end of the Cretaceous period (66.04 Ma) was punctuated by a mass extinction event with the disappearance of nearly 75% of all species. Despite its relevance, the extinction in shallow marine environments is still not fully understood. The present study examines the changes in the Larger Benthic Foraminiferal (LBF) assemblages across the Cretaceous/Paleogene (K/Pg) boundary in the Dunghan Formation, N-E Baluchistan Basin, Pakistan (Eastern Neo-Tethys). The Mehrab Tangi section is known for having one of the most extensive and well-preserved K/Pg transitions deposited in a shallow marine environment in this part of the Eastern Neo-Tethys. Thus, the biotic change in the LBF assemblages can be attributed to two distinct phases, namely the pre-K-Pg phase and the post-K-Pg phase. The pre-K/Pg phase is identified by the presence of the Maastrichtian Omphalocyclus-Orbitoides-Lepidorbitoides assemblages, while the post-K/Pg phase is marked by the Danian Rotalia-Lockhartia assemblages. Based on the composition of LBF assemblages and sedimentological characteristics, five distinct microfacies are identified, which represent different paleoenvironments within the inner-outer ramp. The initial phase of the post-K/Pg period is characterized by the presence of the Rotalia-Lockhartia wackstone, which indicates a rise in sea level. This rise is accompanied by a significant increase in glauconite mineral and fecal pellets, indicating a high rate of sedimentation during the earliest Danian. The K/Pg boundary event had a significant impact on the composition of LBF assemblages in shallow marine deposits globally. This event led to the extinction of several LBF Maastrichtian species, including Omphalocyclus macroporus, Lepidorbitoides blanfordi, Lepidorbitoides minor, Orbitoides media, and Orbitoides apiculate, followed by the appearance of important Danian LBF species such as Rotalia hensoni, Rotalia orientalis, Smoutina cruysi, Lockhartia cushmani, and Lockhartia diversa. The presence of a 1.5-meter-thick laterite band below the Maastrichtian Omphalocyclus-Orbitoides-Lepidorbitoides assemblages provides strong evidence for the onset of the collision between the Indian and Eurasian plates in this region of the Neo-Tethys. Based on detailed and accurate biostratigraphic data, we speculate that the Indian-Eurasian plate collision commenced during the middle Maastrichtian age rather than at the Maastrichtian/Danian boundary as previously suggested in studies conducted in the Himalayas, Eastern Neo-Tethys.
A high-resolution (1d, 9km) and long-term (1950-2022) gridded evapotranspiration dataset
Authors: Xu Qingchen , Li Lu , Wei Zhongwang
Published: March 2024

Evapotranspiration (ET) is the second largest hydrological flux over land surface and connects water, energy, and carbon cycles. Quantifying spatio-temporal ET variability remains greatly challenging due to limited site observations and significant model uncertainties. To address this issue, we develop a multimodal machine-learning framework integrating diverse machine-learning approaches and various available ET datasets to produce high-resolution, long-term ET estimates. We combine direct site observations and 13 different ET products that span remote sensing, machine-learning outputs, data fusion techniques, land surface models, and reanalysis datasets to create fused datasets. Our machine-learning framework integrated cutting-edge tools such as Automated Machine Learning (AutoML), Deep Neural Networks (DNN), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) algorithms to rigorously evaluate model efficacy. Our product exhibits a significantly improved spatiotemporal resolution (0.1 degree, daily) and extended temporal coverage (from 1950 to 2022) compared to existing datasets. In summary, this novel data integration framework overcomes previous ET data limitations through improved quality, spatiotemporal resolution, coverage, and advanced machine learning techniques. The resulting product will enable more accurate ET estimates for water, energy, and carbon cycle applications.
Hybrid modelling framework for photosynthesis rate estimation from leaf to global scales: integrating the spectrum-based V25cmax simulator into the process-based model
Authors: Hu Xiaolong , Shi Liangsheng , Deng Xianzhi
Published: March 2024

Photosynthesis rate is the key element in the carbon cycle process. Accurate photosynthesis rate estimate hinges on the maximum carboxylation rate (V25cmax). The high uncertainty in deriving V25cmax has long hampered efforts toward the performance of the photosynthesis models from leaf to global scales. Recently studies suggest a strong relationship between spectral reflectance and V25 cmax,0. We proposed the spectrum-driven V25cmax simulator using deep learning methods and built the hybrid modelling framework for photosynthesis rate estimation by integrating the data-driven V25cmax simulator in the process-based model. The performance of hybrid photosynthesis models was evaluated at leaf, field and global scales. At the leaf scale, we developed a novel deep learning architecture, which incorporated spatial attention and prior knowledge of spectral indices calculation modules, to extract the V25cmax from leaf hyperspectral images. At field scale, we combined the high-resolution unmanned aerial vehicle (UAV) multispectral imagery and convolutional neural networks (CNN) to estimate V25cmax at the paddy field. At a global scale, we utilized a fully connected deep neural network (DNN) to construct the satellite multispectral-driven V25cmax model based on the FLUXNET2015 dataset. Our result showed that spectrum information can accurately estimate V25cmax. The hybrid framework fully extracts the information of all available spectral bands using deep learning to reduce parameter uncertainty while maintains the description of the photosynthetic process to ensure its physical reasonability. We also highlighted the significance of spatial heterogeneity for V25cmax estimation. This study provides new insight into monitoring photosynthesis rate across different spatial scales.