bluuebunny/crossref_metadata_2025 huggingface dataset:
Trade-offs of simplified versus comprehensive representation of mineralogy when studying dust impacts on Earth’s climate systems
Authors: Ginoux Paul , Song Qianqian , Gonçalves Ageitos María , Miller Ron L. , Obiso Vincenzo , Pérez García-Pando Carlos
Published: March 2024

The intensity and direction of dust impacts on Earth’s climate systems depend on mineral composition. For example, the presence or absence of a few percent of iron oxides in dust will determine if dust is warming or cooling the atmosphere. Similarly, feldspar will enhance ice cloud formation, while acid gases in the atmosphere will react on the surface of dust calcite limiting acid rain. Still, most climate models use a simplified representation of dust mineralogy. They assume a fixed composition at emission which stays invariant during transport and removal. Such simplification assumes spatially and temporally constant physical and chemical properties of dust, and appears to provide satisfactory results when comparing some properties with observations. The trade-off is their lack of spatial gradients, which will fail to induce circulation, cloud and precipitation changes. The two reasons to omit mineral variations are the uncertainty of current atlases of soil mineral composition in arid regions, and, more practically, an improved runtime efficiency. The former reason is losing ground with the recent launch (July 2022) of a dedicated mission (NASA/JPL EMIT) to retrieve global soil mineralogy of dust sources at high spatial resolution.While the EMIT science team is finalizing a satisfactory global map of mineral composition of dust sources, we analyzed the interaction of dust mineralogy on radiation and its impact on the fast temperature response using different representations of mineral composition from detailed and spatially varying to simplified and globally uniform, assuming different hematite contents and methods to calculate optical properties.  Our results show that resolving dust mineralogy reduces dust absorption, and results in improved agreement with observation-based single scattering albedo (SSA), radiative fluxes from CERES (the Clouds and the Earth’s Radiant Energy System), and land surface temperature from CRU (Climatic Research Unit), compared to the baseline bulk dust model version. It also results in distinct radiative impacts on Earth’s climate over North Africa. From our 19-year simulation, we will show that it leads to a reduction of over 50% in net downward radiation at top of atmosphere (TOA) across the Sahara and an approximately 20% reduction over the Sahel. We will explain how the surface temperature response affects the monsoon flow from the Gulf of Guinea.Interestingly, we find similar results by simply fixing the hematite content of dust to a globally uniform value of 0.9% by volume. We will discuss the underlying reasons for such results and show that they may be unrelated to the distribution of soil mineralogy. Still, an accurate representation of soil mineralogy is necessary to better understand dust impacts on the Earth’s climate systems.
Testing Bayesian Network transferability to diverse agricultural catchments with high phosphorus saturation
Authors: Negri Camilla , Schurch Nicholas , Wade Andrew J. , Mellander Per-Erik , Glendell Miriam
Published: March 2024

A Bayesian Network (BN) aimed at calculating stream phosphorus (P) concentrations in agricultural catchments was previously parametrized with high-frequency data in a pilot study. To test model transferability, the BN was applied to three further agriculture-dominated catchments in Ireland with varying land use, hydrology, and P pressures, all monitored through the Agricultural Catchments Programme (ACP) of the Irish Agriculture and Food Development Authority. While the pilot catchment Ballycanew was dominated by poorly drained grassland, the further three catchments were dominated by well-drained grassland (Timoleague), well-drained arable (Castledockrell), and moderately-drained arable (Dunleer), respectively. In all four catchments, the main P source came from agriculture and (minimal) domestic inputs, whilst the well-drained arable catchment also contained Sewage Treatment Works (STWs).To best fit the characteristics of the catchments, a total of six different BN structures were developed. The models were parametrized using a range of methods, including bootstrapping of high-frequency data to obtain fitted distributions, distribution fitting of literature data, and expert elicitation to quantify in-stream P uptake processes. Model transferability and fit were evaluated using a suit of approaches, including 1) calculating percentage bias between simulated and observed distributions fitted to the observed stream Total Reactive P (TRP) concentration, 2) comparing modelled concentration quantiles and means to the observed, and 3) visually comparing the posterior distributions by plotting them against daily observations.The original BN structure developed in the pilot study was found to best fit the poorly and moderately drained catchments, irrespective of the dominant land use (78% ≤ PBIAS ≤ 81%), not as well in the groundwater-dominated catchments. This confirms that the initial BN represents the catchment-specific process understanding whereby transport via quick-flow dominates P processes in these catchments. In contrast, the well-drained catchments required more complex BN structures to perform well. The additional processes included groundwater Total Dissolved P (TDP) loads, derived from observed concentrations from piezometer data, STWs loads, and in-stream P uptake calculations. These more complex model implementations yielded good results in Castledockrell and Timoleague (-5% ≤ PBIAS ≤ 14%). In all four catchments, the additional in-stream P removal process improved the model performance, however, it remains a second-order mechanism.Overall, the unique monitoring programme allowed pilot-testing BN transferability, a research avenue that needs to be further explored across catchment typologies and scales.
Direct measurement and indirect estimation of unsaturated soil hydraulic properties in Tunisian soils.
Authors: Hmaied Asma , Hammecker Claude
Published: March 2024

AbstractThe hydrological cycle is strongly affected by climate changes causing extreme weather events with long drought periods and intense rainfall events. To predict the hydrological functioning of Tunisians catchments, modelling is an essential tool to estimate the consequences on water resources and to test the sustainability of the different land uses. Soil physical properties describing water flow, are therefore essential to feed the models and need to be determined all over the watershed.In order to complete this task, lightweight, cost effective but robust methods are needed. In the present study, both physically based and empirical models or pedo-transfer functions (PTF) have been used to estimate unsaturated soil hydraulic properties based on particles size distribution (PSD), and straightforward in-situ infiltration experiments.The specific Pedo-Transfer Functions (PTFs) embedded within the Rosetta model, the physically grounded Arya-Paris model, and the Beerkan Estimation of Soil Transfer parameters (BEST) have been specifically developed to gauge soil hydraulic parameters based on soil texture, bulk density, and, eventually, outcomes from single-ring infiltration experiments. These models were applied to a diverse array of soil types from both Northern and Central Tunisia, with a subsequent comparative analysis aimed at evaluating their potential applicability and individual performances.Consequently, the estimated parameters derived from these models were incorporated into Hydrus to compute water flow in the vadose zone under the actual weather conditions prevailing in Tunisia. The resultant effects on the calculated water balance, encompassing infiltration, drainage, and runoff, were systematically compared for a comprehensive understanding of their implications.Results show that soil hydraulic parameters determined with different techniques are significantly different. The results for simulated water balance over 3 years, show also differences especially for intense rainfall events. It seems that the BEST method is a valuable technique for estimating soil hydraulic parameters, offering a cost-effective and practical alternative to traditional methods, especially as it leverages on experimental infiltration data. 
The Evolving Relative Role of Stratospheric Ozone and Greenhouse Gasses in Modifying the Southern Ocean Carbon Sink from 1950-2
Authors: Jarníková Tereza , Le Quéré Corinne , Rumbold Steven , Jones Colin
Published: March 2024

Southern Ocean winds have strengthened and moved poleward in the latter half of the 20th century, which has been attributed to the depletion of stratospheric ozone and to climate warming from rising greenhouse gas concentrations. Both ozone recovery and changing greenhouse gas concentrations are expected to continue modulating wind structure throughout the 21st century. Here, we quantify the relative roles of ozone and greenhouse gases on Southern Ocean wind structure from 1950-2100 using the UK Earth System Model (UKESM1) model output, with a combination of three scenarios of ozone and two scenarios of greenhouse gas evolution. Both ozone depletion and increases in greenhouse gas concentration act to increase wind speed over the Southern Ocean. The influence of ozone is predominant in summer winds, while the influence of greenhouse gases acts in all seasons. We show that wind speeds return close to their original levels by the end of the 21st century under a low-greenhouse gas scenario with ozone recovery. The influence of ozone on wind speed was dominant in the 1950-2000 time-period, but not in the 21st century when the influence of greenhouse gases becomes two to three times larger than that of ozone, even in the low emissions scenario. We find significant effects of both ozone scenario and greenhouse gas emissions on physical-oceanographic variables (sea surface temperature, mixed layer depth, and overturning circulation). Finally, we quantify the relative contributions of these physical changes to the evolving carbon sink of the Southern Ocean, and discuss how wind-induced physical changes can alter ecosystem processes and the associated carbon export to the deep ocean.
Rainfall downscaling using stochastic generators and machine learning
Authors: del Jesus Manuel , Diez-Sierra Javier , Navas Salvador
Published: March 2024

Daily rainfall records are the most common form of rainfall information. These records are the ones normally used to characterize the extremes of rainfall. However, in many situations, sub-daily information is required, normally to characterize the extreme response of small watersheds. Different methods exist to extrapolate the daily information to smaller time scales -normally, hourly time scales-, which tend to be based on a limited number of finer than daily observations.In this work, we will deal with two common downscaling problems that the hydrologist faces: transforming daily rainfall observations into estimates of sub-daily rainfall statistics and incorporating climate change information into these estimates. Although generally, these two procedures are different, both conceptually and mechanically, we will combine stochastic generators and machine learning to create a unified framework where both problems are connected and solved in a similar manner.We will use NEOPRENE (Diez-Sierra et al., 2023), a Python-based open source library that implement the Nyeman-Scott, or Cox and Isham, stochastic model of rainfall (Cox & Isham, 1988) to characterize the rainfall process, and random forests to relate daily and hourly rainfall statistics (del Jesus & Diez-Sierra, 2023). The model assumes a geometric description of the rainfall process, that allows to decompose observed time series and reproduce several statistics at different levels of aggregation.We will also demonstrate how downscaling can be carried out to generate plausible hourly rainfall distributions from daily ones, and how this process serves to characterize the uncertainties of the estimates.Cox, D. R. & Isham, V., 1988. A Simple Spatial-Temporal Model of Rainfall. Proceedings of the royal society a: Mathematical, physical and engineering sciences, 415 ​(1849), 317–328.Diez-Sierra, J., Navas, S. & Jesus, M. del., 2023. NEOPRENE v1.0.1: A Python library for generating spatial rainfall based on the NeymanScott process. Geoscientific model development, 16 (17), 5035–5048.Jesus, M. del & Diez-Sierra, J., 2023. Climate change effects on sub-daily precipitation in Spain. Hydrological sciences journal, 68 (8), 1065–1077.
A multidisciplinary and multi-institutional fieldwork in the Indian Himalaya for glacio-hydro-climatological studies
Authors: Vijay Saurabh , Rashid Irfan , Banerjee Argha , Sarangi Chandan
Published: March 2024

Himalaya is the longest mountain range in high-mountain Asia. The Himalaya is a home of thousands of glaciers that provide freshwater to a large population living in these countries. Glaciers are also a key indicator of regional and global climate change. Therefore, they are studied by a diverse set of researchers including glaciologists, climate scientists and hydrologists. Although satellite remote sensing and modelling communities have grown to address past, present and future changes in glaciers, field based studies are still vital. As the Himalayan range is shared by many bordering countries including India, China, Pakistan and Nepal, the strategies of conducting fieldwork vary depending on financial resources and trained manpower. As the fieldwork is time-consuming and expensive, new approaches are required.   In this work, we show how we formed a  glaciological community of early-career permanent faculty or scientists in India to plan and conduct extensive fieldwork in a cost-effective manner. In the last three years, this group has conducted more than 5 joint field expeditions in the Indian Himalaya. Here, we highlight the challenges of multi-disciplinary and multi-institutional fieldwork. India is a huge country with diverse cultures, habits and languages. Different institutions have different policies of sharing field equipment. Proper planning and time management are critical, but not everyone, especially first-timers, do not understand their role in practice, which makes it very difficult for the field managers. Consistent measurements at the benchmark locations are very important, but this is often challenging as the institute/principal investigator-wise funding is limited and time-varying. Overcoming this scenario, this group developed a multi-institutional funding with efficient and resource sharing plan to set up a benchmark site in the Himalaya, which can be used for consistent monitoring for more than 10 years and address key science questions related to glaciology, hydrology and micro-climate. Such a project can be joined by any institute across the world and the partnering institute may add value by adding measurement plans and science objectives as well as benefit from existing capacities at the benchmark location. This group has previously hosted research partners from Germany and Australia. Some group members worked with several research groups and acted as a bridging partner between Indian and non-Indian researchers. A bridging partner played an important role to handle aspects related to expectations, working culture and training. In short, this study highlights the successes and challenges of such an efficient consortium that promote international collaboration, consistent monitoring and training of students in the field as well as knowledge and manpower exchange.     
Evaluating the importance of nitrate aerosol in the upper troposphere and lower stratosphere during the Asian summer monsoon season
Authors: Zhu Yunqian , Yu Pengfei , Bardeen Charles , Wang Xinyue , Borrmann Stephan , Höpfner Michael , Deshler Terry , Bian Jianchun , Bai Zhixuan , Portmann Robert , Rosenlof Karen , Kloss Corinna , Clegg Simon , Wexler Anthony , Pan Laura , Smith Warren , Toon Owen
Published: March 2024

The Asian Summer Monsoon (ASM), as well as tropical convection, transport aerosols and their precursors from the boundary layer to the upper troposphere and lower stratosphere (UTLS). We utilize the Community Aerosol and Radiation Model for Atmospheres (CARMA) coupled with the Community Earth System Model (CESM) to simulate all major tropospheric aerosols including sulfate, organics, ammonium, nitrate, sea salt, and dust. We evaluate the model during the ASM season by comparing the simulated aerosol microphysical properties, such as particle compositions, size distribution, and particle volume with MIPAS satellite and in-situ data from three field campaigns. We find nitrate, organics, and sulfate contribute significantly to the UTLS optical properties between 0-45˚N, 0-180˚E. The major source of nitrate is the ammonium nitrate formed locally and nitric acid condensation near the cold tropopause. Including nitrate in the model doubles the surface area density between 0-45˚N, 0-180˚E, which alter the chlorine partitioning at the UTLS region.
Connecting the Carbon and Water Cycles through Vegetation   
Authors: Verma Akash , Ghosh Subimal
Published: March 2024

Socioeconomic growth in India has resulted in a substantial increase in carbon dioxide (CO2) emissions. Despite this, India emerges as the second-largest contributor to global greening, as revealed by remote sensing datasets. These conflicting factors pose a unique challenge in understanding the variability of atmospheric CO2 and its implications for global warming. The present study aims to address this research gap by presenting the first analysis of climate controls on carbon flux variability in India. Our key objectives are (1) to identify the climate drivers influencing the variability of vegetation productivity in agriculture-dominated India and (2) to understand the implications of increased plant growth on water availability by analyzing the CO2 fertilization effect. Unlike previous studies, we have not used simplistic estimates like partial correlation for causality; instead, we employed a recent tool, PCMCI, designed explicitly for detecting causality. In contrast to global studies, we find no causal connection between terrestrial water storage and vegetation productivity. Our results suggest that precipitation plays a significant role in the Indian region rather than deep groundwater, due to its immediate impact on shallow-rooted vegetation. Our findings highlight the significance of land use, land cover, and distinct irrigation practices— aspects often overlooked in current land surface models. Furthermore, we are investigating the response of soil moisture to CO2 fertilization via two pathways: increased leaf area index (LAI) and enhanced water use efficiency (WUE) using state-of-the-art CMIP6 simulations. We are evaluating whether WUE can ameliorate plant water stress, especially when the LAI can counteract its impact by increasing transpiration. The present study adopts a holistic approach to demonstrate the critical interaction and feedback between climate controls, vegetation, and CO2 fertilization, thereby significantly improving our understanding of land-atmosphere interaction.Keywords: Climate controls, CO2 fertilization, Soil moisture, Vegetation productivity, Causal discovery
Leveraging Crowd-sourced Biodiversity Data for an Enhanced Plant Functional Trait Mapping
Authors: Moreno-Martínez Alvaro , Muñoz-Marí Jordi , Adsuara Jose E. , Dechant Benjamin , Katge Jens , Kattenborn Teja , Maria Sabatini Francesco , Butler Ethan , van Bodegom Peter  M. , Schneider Fabian D. , Mahecha Miguel , Peñuelas Josep , Townsend Philip A. , Boenisch Gerhard , Izquierdo-Verdiguier Emma , Carvalhais Nuno , Duveiller Gregory , Lusk Daniel , Camps-Valls Gustau
Published: March 2024

Plant functional traits play a crucial role in determining how terrestrial ecosystems function. However, most Earth system models (ESMs) oversimplify this information, representing it with a limited number of static, empirically fixed values assigned to a selection of plant functional types (PFTs). This results in a reduction of the diversity of plant communities into a relatively small number of categories and the loss of key variability within individual PFTs. As a result, local processes occurring within ESM grid cells are not well represented, leading to uncertainties in predicting ecosystem functions.The TRY global plant traits database is home to the most extensive collection of in-situ trait observations for a broad spectrum of species across the globe. Nonetheless, despite the numerous species and samples included in TRY, it still falls short compared to the overall richness and diversity of species and ecosystem functions worldwide. As a result, various initiatives have emerged to create global maps of plant traits. In this study, we created maps of essential plant traits, such as specific leaf area (SLA), leaf nitrogen content (LNC), and leaf phosphorus content (LPC), at a spatial resolution of 1 km. We took an innovative approach by leveraging the use of biodiversity, trait databases, and remote sensing data as primary sources of information. Additionally, we provide ancillary data layers that indicate regions where  data gaps currently exist and  where more samples are needed to improve trait representation in TRY.We compared our results to plot-level estimates for thousands of sites globally. The comparison demonstrated strong correlations (r > 0.5) and low relative errors (rME < 6% and rRMSE < 11%) for all considered traits despite the challenges in scaling up from local to global scales. Our results reveal the non-Gaussian nature of trait distributions at a global scale when computing community representative mean trait values and further statistical descriptors, including standard deviation, skewness, and kurtosis estimations. These higher-order moments provide a more detailed and nuanced view of plant functional diversity and distribution. Using these new data to parameterize global ecological models could lead to more accurate predictions and a better understanding of the main drivers of different ecosystem processes.
A full probabilistic approach to landslide forecast
Authors: Ferriero Flavia , Marzocchi Warner , Urciuoli Gianfranco , Mancini Simone
Published: March 2024

Landslides are among the most destructive natural disasters that occur frequently worldwide, claiming lives and causing severe economic losses. The most common approaches for managing the short-term landslide risk is based on the definition of deterministic thresholds of a triggering event (a seismic quantity, or an amount of rain) above which the landslide is expected to occur. However, landslides, as well as most of natural events, is hardly predictable deterministically, owing to the unavoidable and ubiquitous presence of uncertainties of different kind. In this study, we present the first steps towards the development of a full probabilistic landslide forecasting model that accounts for the probabilistic forecasts of triggering events (such as earthquakes and/or rainfalls), and it includes a full appraisal of different kinds of uncertainty. Within a Bayesian mathematical framework, the model combines the probabilistic distribution of the mechanical parameters of the soil with the probability of observing a certain natural triggering event; the output is a space-time dependent probability of occurrence of landslides as a function of the probability of occurrence of their triggering event. In addition, we describe the landslide forecasts as a distribution of probability instead of one single value, to give a complete description of what we know and what we do not know. This approach provides a suitable scientific output that can be used by land use managers and decision-makers. Indeed, a formal probabilistic assessment fits more adequately the intrinsic non-deterministic nature of landslide occurrence. Moreover, it provides a more suitable framework that help defining  roles and responsibilities of all actors involved in the full risk reduction process.
Numerical dye tracer experiments in Bedford Basin in support of Ocean Alkalinity Enhancement research
Authors: Wang Bin , Laurent Arnaud , Fennel Katja
Published: March 2024

Ocean Alkalinity Enhancement (OAE) is considered as a potential technique to mitigate ocean acidification and remove carbon dioxide (CO2) from the atmosphere. In this study, a suite of numerical tracer experiments was conducted using a high-resolution nested model to support ongoing OAE field trials in Halifax Harbor and Bedford Basin. We first estimated the residence time, which provides an overall description of the circulation, for different seasons over the past 20 years (2003-2022). Results show a clear seasonal pattern in residence time which is longest in July and shortest in January. Particles with different dissolution rates and sinking velocities were then added continuously through the cooling outfall of a local power plant for three months to simulate the dissolution, dispersion, and movement of different alkaline mineral feedstocks. To account for inter-annual variability, the years with the longest and shortest residence time in each season were selected to perform these simulations. Furthermore, tracer simulations will be compared with ongoing Rhodamine WT field trials. Results obtained thus far show that the surface alkalinity signal due to OAE is most likely to be detected near the cooling outfall but depends on the tidal stage and the local circulation and weather conditions. Detectability is highest in July because the residence time is longest. In addition, the detectability increases with faster dissolution rate and slower sinking velocity. 
Seasonal predictions of summer humid heat extremes in the southeastern United States driven by sea surface temperatures
Authors: Jia Liwei , Delworth Thomas , Yang Xiaosong
Published: March 2024

Humid heat extreme (HHE) is a type of compound extreme weather event that poses severe risks to human health. Skillful forecasts of humid heat extremes months in advance are essential for developing strategies to help communities build more resilience to the risks associated with extreme events. This study demonstrates that the frequency of summertime HHE in the southeastern United States (SEUS) can be skillfully predicted 0-1 months in advance in the SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. The sea surface temperature (SST) at the tropical North Atlantic (TNA) basin is found as the primary driver of the prediction skill. The responses of large-scale atmospheric circulation and winds to anomalous warm SSTs in TNA favor the heat and moisture flux transported from the gulf of Mexico to the SEUS. This research demonstrates the role of slowly-varying sea surface conditions in modifying large-scale environments that contribute to the predictions of HHE in SEUS. The results are potentially applicable for developing early warning systems of HHE. 
Exploring CORDEX and driving GCM differences, in the context of time evolving aerosols 
Authors: booth ben , Crocker Tom , Solis Salas Citlali , McSweeney Carol , Andrews Tim
Published: March 2024

An emerging literature has been exploring RCM-GCM differences over Europe and linking many of these differences to the lack (or in a few cases, underestimation) of aerosol “Brightening” in the RCMs (Boe et al, 2019, Gutiérrez et al, 2020, Tarana et al, 2022).  Here we illustrate these differences for mid-century European surface SW, temperature and rainfall projections, with a focus on the spread.  The tendency for the RCMs to underestimate the GCM warming and drying is evident.  For example the RCMs at the lower end show roughly half the warming of the lowest driving CMIP5 GCM and roughly 50% of the RCMs suggest wetter projections that 6 out of 7 driving CMIP5 GCMs.  We extend this analysis to the wider CORDEX regions and identify where lack of time varying aerosol representation does, and does not matter.   We also use single forcing (AER) CMIP6 experiments, to illustrate how this data can be a useful tool to predict where time varying aerosol representation is (and where it is not) likely to be important in ongoing CMIP6 downscaling.
Statistical and neural network-based AOD data fusion with Geostationary satellite instruments: GEMS, AMI, and GOCI-II.
Authors: Kim Minseok , Kim Jhoon , Lim Hyunkwang , Lee Seoyoung , Cho Yeseul , Lee Yun-Gon , Go Sujung , Lee Kyunghwa
Published: March 2024

Aerosol optical depth (AOD) data fusion for aerosol datasets obtained from the Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT; GK) series was conducted through the application of both statistical and deep neural network (DNN)-based methodologies. The GK mission incorporates the Advanced Meteorological Imager (AMI) on GK-2A, as well as the Geostationary Environment Monitoring Spectrometer (GEMS) and Geostationary Ocean Color Imager-II (GOCI-II) on GK-2B. The statistical fusion approach rectified biases in each aerosol product by assuming a Gaussian error distribution. Utilizing Maximum Likelihood Estimation (MLE) fusion, the technique accounted for pixel-level uncertainties by weighting the root-mean-square error of each AOD product for individual pixels. A DNN-based fusion model was trained to align with Aerosol Robotic Network AOD values through fully connected hidden layers. The results of both statistical and DNN-based fusion generally surpassed the performance of individual GEMS and AMI AOD datasets in East Asia (R = 0.888; RMSE = −0.188; MBE = −0.076; 60.6% within EE for MLE AOD; R = 0.905; RMSE = 0.161; MBE = −0.060; 65.6% within EE for DNN AOD). Particularly, focusing on AOD around the Korean peninsula, encompassing all aerosol products, yielded significantly improved outcomes (R = 0.911; RMSE = 0.113; MBE = −0.047; 73.3% within EE for MLE AOD; R = 0.912; RMSE = 0.102; MBE = −0.028; 78.2% within EE for DNN AOD). The DNN AOD demonstrated effective handling of the rapid increase in uncertainty at higher aerosol loadings. Overall, the fusion AOD, particularly DNN AOD, closely matched with the performance of the Moderate Resolution Imaging Spectroradiometer Dark Target algorithm, exhibiting slightly less variance and a negative bias. Both fusion algorithms stabilized diurnal error variations and provided additional insights into hourly aerosol evolution.
Towards AI-driven real-time deposit modeling: a case study in southern Brazil
Authors: Gonçalves Ítalo , Frigo Everton
Published: March 2024

Mineral exploration and resource estimation play pivotal roles in the mining industry, driving the need for accurate and comprehensive data about mineral deposits. Traditionally, drill core samples have been the primary means of obtaining crucial information regarding the size, shape, and mineral composition of deposits. However, the cost associated with drilling limits the number of samples that can be acquired, posing challenges to achieving a thorough understanding of a mineralized area. In response to these challenges, the mining industry is increasingly turning to cutting-edge technologies to enhance exploration efficiency and reduce costs, such as aerial photogrammetry, hyperspectral imaging, and core scanning. These technologies offer the advantage of acquiring data over larger areas in a relatively short period, providing valuable insights into the geological characteristics of a site. In the context of developing mines, each round of blasting uncovers fresh rock surfaces that harbor new geological information. Leveraging this opportunity to gather real-time data presents an exciting prospect for optimizing mineral exploration. By systematically collecting and processing information from newly exposed surfaces, it becomes possible to enhance the insights obtained from conventional core samples. This research introduces a case study exemplifying the implementation of this paradigm shift in a marble quarry located in southern Brazil. Here, a convolutional neural network is employed to interpret geological features in aerial photographs. Subsequently, the interpreted data is fed into a photogrammetry software, generating a labeled point cloud that complements information derived from traditional core samples. The synergy between aerial data and core sample information allows for the creation of highly detailed lithology models, enabling more accurate short-term forecasting of stripping ratios. A key aspect of this work involves the development and utilization of an in-house Gaussian process implementation for lithological modeling. This technique not only provides insights into the size and shape of the orebody but also offers an invaluable uncertainty estimate. The results from this case study demonstrate the potential of this paradigm shift in mineral exploration and mining practices. Ultimately, this research aims to showcase a pathway towards a more economical, environmentally friendly, and sustainable future for the mining industry.
On the impact of timber harvesting on soil water retention and surface runoff
Authors: Behringer Max , Scheidl Christian , Markart Gerhard , Meißl Gertraud , Froemel Marcus , Gasser Lisa , Grünberg Julian , Haas Christoph , Hofbauer Armin , Kitzler Barbara , Kühmaier Martin , Nemestothy Nikolaus , Rewald Boris , Wieshaider Alexandra , Katzensteiner Klaus
Published: March 2024

Structural properties of undisturbed soils are critical for water retention and the reduction of peak flows after heavy rainfall events. Forest soils commonly show high infiltration rates that can be attributed to a high organic content and the formation of larger pores through biological activity. Though soil disturbances, especially soil compaction, due to timber logging can be considered a rare event, the impacts may be long lasting. The productive, often fine textured soils of the Alpine Flysch belt are particularly susceptible to compaction, posing a challenge for timber harvesting.In a controlled experiment in the Flysch zone (Vienna Woods, Austria), we assessed the effects of different timber harvesting technologies – specifically harvester-forwarder (with or without bogie tracks) and chain saw-cable yarder – on soil functions. For the quantification of the surface runoff, we applied rainfall simulation experiments on seven plots of 50 m² each. All rainfall simulation experiments were conducted for one hour with a targeted intensity of 100 mm/h before and after harvesting. Within each irrigation plot, we sampled undisturbed soil cores at up to five depth levels (5, 15, 25, 40, 65 cm) for further analyses in the laboratory. We measured saturated hydraulic conductivity (KSAT device; METER Group, Munich, Germany), as well as soil water retention in the wet and medium soil moisture range using the HYPROP device (METER Group, Munich, Germany). In the dry soil moisture range (pF>4.2) we measured water retention with the dew point method using the WP4C device (METER Group, Pullman, USA). Additionally, soil texture and soil organic carbon were determined from the same soil samples.Preliminary results suggest a strong impact of the harvester-forwarder system (w/wo bogie tracks) on all hydrologically effective soil properties, while the cable yarder system seems to have lower, yet still noticeable impacts. For the log10 of the saturated hydraulic conductivity (log10KS) the harvester-forwarder treatments cause significantly lower values, with reductions of up to >99% compared to values prior to harvesting. The decline of log10KS in cable yarding systems is only marginally significant (up to -49%). First order analyses of runoff coefficients show a strong effect of the harvester-forwarder system with observed values of up to 0.66. Undisturbed sites had no surface runoff and cable yarding only generated minimal surface runoff. 
Assessment of spectroscopic instruments for continuous atmospheric nitrous oxide monitoring
Authors: Emmenegger Lukas , Zellweger Christoph , Steinbacher Martin
Published: March 2024

In recent years, the field of laser spectroscopy has witnessed significant progress, leading to major advancements in the detection of atmospheric trace gases. This technological evolution is reflected in a growing number of commercial implementations, especially for prevalent atmospheric gases, such as carbon dioxide (CO2) and methane (CH4). The spectrum of detectable trace gases continues to expand, and manufacturers offer instruments with increasing performance in term of selectivity, sensitivity, power consumption, compactness and cost-effectiveness.In this presentation, we focus on recent instruments for the observation of atmospheric nitrous oxide (N2O). N2O is a major long-lived greenhouse gas which plays an important role in stratospheric ozone depletion, but still suffers from inadequate global data coverage. Therefore, the advent of more economical yet resilient instruments, demanding less space and power compared to conventional models, presents a welcome opportunity to broaden the N2O monitoring network.We provide an overview of laboratory tests carried out at Empa on a variety of commercial models. The evaluated techniques include (i) Mid-IR Tunable Diode Laser Spectrometry (TDLAS) with Interband Cascade Lasers (ICLs) and Quantum Cascade Lasers (QCL), (ii) Optical Feedback – Cavity Enhanced Absorption Spectroscopy (OF-CEAS), (iii) Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS), and (iv) Cavity Ringdown Spectroscopy (CRDS).The tests assessed the suitability of the instruments for precise atmospheric N2O monitoring and provide insights into the operation, data handling and quality assurance / quality control procedures required for long-term operation. Particular attention was paid to the evaluation of the short-term precision and stability of the instrument response and the repeatability within days to weeks.Overall, the instrument performance is still superior for the most-established CRDS and OA-ICOS analyzers, which are widely used in the Global Atmosphere Watch (GAW) programme and the European Integrated Carbon Observation System Research Infrastructure (ICOS-RI). Nevertheless, the latest generation of TDLS and OF-CEAS instruments are cost-efficient alternatives, which may be suited for more extensive networks, such as the ones to be designed under the umbrella of World Meteorological Organization's new Global Greenhouse Gas Watch (G3W) programme. However, great care needs to be taken in terms of quality assurance and quality control (QA/QC) to ensure long-term accuracy and traceability. The most cost-efficient instrumental components still need to be identified as a function of the scientific targets and the related network design.
Climate Change Impacts on Active Zone Groundwater Dynamics in the High Arctic, Canada
Authors: Stribling Selsey , McKenzie Jeffrey , Lamontagne-Hallé Pierrick , Novosad Nathaniel , Hemmings Dylan , MacNeil Tom
Published: March 2024

With Arctic amplification, the rate of Arctic warming is estimated to be between two to four times greater than at lower latitudes. Northern warming is leading to environmental change, including permafrost thaw and changes in groundwater flow due to alterations in the timing and the depth of the active zone. Research suggests that, due to permafrost degradation and concomitant increased groundwater mobility, exfiltration to northern groundwater-fed lakes may increase with continued warming. Many parts of the terrestrial Arctic are experiencing warming and increased precipitation, both of which affect both the annual timing of formation and depth of the active zone, thereby controlling the amount of water that may be transmitted through the shallow subsurface. The objective of our research is to use a numerical modeling approach to disentangle the effects of changes in precipitation and warming for a site in the Canadian High Arctic (63°30′N).Through an archetypal modeling approach for a site with limited field data, we use SUTRA 4.0 to simulate groundwater flow and energy transport with dynamic freeze-thaw processes. To assess active layer zone changes, we simulate a two-dimensional 280 m long hill underlain by continuous permafrost that terminates in a lake. The site has thin unconsolidated overburden on bedrock, with current depth to permafrost between 1.3 m and 2.2 m. We simulate four cases using downscaled CMIP5 projections: modern conditions, near climate (2020s), mid-climate (2050s), and far-climate projection (2080s). The climate projects show increasing annual mean temperatures, decreasing annual temperature amplitude, and increasing precipitation. The groundwater model results primarily focus on the groundwater flux to the lake, as it integrates the flows across the entire system. The results show that there will be increasing flows of groundwater to the lake due to climate change. Further, the increase in mean annual temperature (as opposed to increased precipitation) and associated annual development of the active zone is the primary control on groundwater flow through the system. With warming, the active zone deepens and opens for a longer period each year, allowing for more groundwater flow, particularly during snowmelt.Understanding active zone changes and groundwater in the Arctic allows us to better assess potential future hydrologic changes and discharge into northern lakes. The results from this study have implications for the potential transport and fate of anthropogenic and geogenic contaminants in Northern environments.
Development of an Autonomous On-Site Dissolved Inorganic Carbon Analyzer using Conductometric Detection Technique
Authors: Bhattacharya Sayoni , Esposito Mario , Tanhua Toste , Achterberg Eric P.
Published: March 2024

Gradual increase of anthropogenic CO2 concentration in the Earth's atmosphere changes the CO2 uptake capacity by seawater, leading to alteration of ocean carbon chemistry and therefore resulting in ‘Ocean Acidification’. Dissolved Inorganic Carbon (DIC) is one of the key parameters among the four primary variables (i.e., pH, partial pressure of CO2 (pCO2), Total Alkalinity (TA), and DIC) along with temperature, salinity, and macronutrients to fully characterize the seawater carbonate system. To improve our quantitative and mechanistic understanding of the marine carbonate system, high-quality and high spatial-temporal resolution observations of DIC are required. To meet these expectations, an autonomous DIC analyzer is needed which is cost-effective, offers high sampling frequency, low reagent as well power consumption. Here we present the development and validation of a novel analyzer for autonomous measurements of DIC in seawater using conductometric detection technique. The presented DIC analyzer employs a gas diffusion flow injection approach in a “Tube In A Tube” configuration that facilitates diffusion of gaseous CO2 from an acidified sample through a gas permeable membrane (Teflon AF2400) into a stream of alkaline solution (NaOH). The change in conductivity in the alkaline medium is measured using a detection cell with 4-hollow brass electrodes and the change in conductivity is directly proportional to the DIC concentration of the sample. Physical and chemical optimizations of the analyzer yielded sample acidification to pH < 4, a NaOH concentration of 7 mM with a flowrate of 300 µL min-1, and an inner diameter of the gas permeable tube of 0.6 mm, allowing DIC measurements in both freshwater and marine systems between 500 and 3000 µmol kg-1. The analyzer can measure 4 samples hour-1 and it requires 0.2 mL of H3PO4, 0.75 mL of NaOH, and 2 mL of sample for each measurement. Temperature and salinity effects were characterized over the ranges 5-35°C and 0-35 in the laboratory, respectively, with the formulation of a mathematical T-S correction for accurate DIC determination. Measurements of a DIC reference material (RM) over four days yielded an analytical precision of ±4.89 µmol kg-1 (n=6) and an accuracy of +1 µmol kg-1. The operational robustness of the system has been demonstrated through a field deployment in the southwest Baltic Sea, yielding an analytical precision of ±9.69 µmol kg-1 (n=6). This study describes an autonomous, on-site, cost-effective DIC analyzer capable of measuring DIC in seawater at a high temporal resolution with an ultimate aim to develop an underwater DIC sensor. The achieved accuracy and precision offer an excellent opportunity to employ the analyzer in CO2 leakage monitoring and detection in the context of Carbon Capture and Storage.  
Mapping the vulnerability to heat: an application in the city of Bern
Authors: Lee Sujung , Burger Moritz , Gubler Moritz , Brönnimann Stefan , Vicedo-Cabrera Ana Maria
Published: March 2024

Background: Heat is widely acknowledged as one of the most hazardous climate-related risk factors affecting human health. Increasing urban development has led to an amplification of its health impacts due to the Urban Heat Island (UHI) effect. However, our understanding of neighbourhood-level vulnerability to the UHI effect remains limited. This information can be crucial for policymakers to identify high-risk areas in cities and develop more targeted public health interventions. Thus, we propose a comprehensive approach to map the vulnerability to UHI in the city of Bern (Switzerland) by (1) assessing the demographic and socio-economic factors contributing to increased UHI exposure and (2) analysing the spatial distribution of vulnerability to the UHI effect.Methods: We collected population and household statistics at the individual level from 2012 to 2021 from the Federal Statistical Office of Switzerland. Firstly, we calculated the intensity of UHI (representing the temperature difference between the inner city and the rural surroundings) in each district of Bern using high-resolution (50mx50m) modelled urban temperature data. Next, we performed univariate logistic regression models to estimate the association between UHI exposure and population characteristics, reporting odds ratio (OR) and 95% confidence intervals (CI). We defined UHI exposure as individuals being exposed to UHI intensity exceeding the city-mean for the corresponding census year. Subsequently, we established the Heat Vulnerability Index (HVI) by selecting key determinants: 1) the elderly population (aged ≥65 years), 2) females, and 3) individuals with low socio-economic status. The overall percentile ranks for districts were calculated by summing variable rankings.Result: First, our study identified several factors contributing to increased UHI exposure, in particular, single individuals had 60% higher odds of UHI exposure (OR:1.60; CI:1.59-1.62) compared to married individuals, and individuals aged 26-44 (1.71; 1.70-1.74) compared to those aged 0-17. Also, wealthier individuals appeared to have higher odds of UHI exposure (medium: 2.32; 2.30-2.35, high: 1.66; 1.64-1.67) compared to the lowest group. In the context of the work environment, individuals in large-size companies (≥250 employees) had an increased risk (1.85; 0.77-6.05) of UHI exposure compared to those in micro-size companies (
A New Machine Learning Approach for Predicting Extreme Space Weather
Authors: Hu Andong , Camporeale Enrico
Published: March 2024

We present an innovative method, ProBoost (Probabilistic Boosting), for forecasting extreme space weather events using ensemble machine learning (ML). Ensembles enhance prediction accuracy, but applying them to ML faces challenges as ML models often lack wellcalibrated uncertainty estimates. Moreover, space weather problems are typically affected by very imbalanced datasets (i.e., extreme and rare events) To overcome these difficulties, we developed a method that incorporates uncertainty quantification (UQ) in neural networks, enabling simultaneous forecasting of prediction uncertainty.Our study applies ProBoost to the following space weather applications:• One-to-Six-Hour Lead-Time Model: Predicting Disturbance Storm Time (Dst) values using solar wind data.• Two-Day Lead-Time Model: Forecasting Dst probability using solar images.• Geoelectric Field Model: Multi-hour lead time, incorporating solar wind and SuperMag data.• Ambient Solar Wind Velocity Forecast: Up to 5 days ahead.ProBoost is model-agnostic, making it adaptable to various forecasting applications beyond space weather.