bluuebunny/crossref_metadata_2025 huggingface dataset:
Doble colédoco, variante anatómica infrecuente de la vía biliar. Presentación de caso
Authors: Borges Anaid
Published: January 2024

La presencia de un conducto colédoco doble es una variante anatómica infrecuente con menos de 200 casos publicados en la literatura hasta la fecha, siendo fundamental los estudios preoperatorios que se le deben realizar a los pacientes, a fin de tener la sospecha o el diagnóstico confirmado, no sólo de la variante anatómica, sino de la patología asociada, siendo estas la coledocolitiasis y las patologías malignas del árbol biliar las más frecuentes. Se presenta el caso de masculino de 81 años de edad, con antecedente de colecistectomía 20 años previos, con diagnóstico de síndrome ictérico obstructivo, y estudios de colangiorresonancia y CPRE que reportan coledocolitiasis, donde es llevado a acto quirúrgico y se diagnóstica la presencia de conducto colédoco doble como hallazgo. Se realiza derivación biliodigestiva con resultado satisfactorio, y resultado de biopsia negativo para malignidad.
KẾT QUẢ PHẪU THUẬT BẮC CẦU ĐIỀU TRỊ BỆNH ĐỘNG MẠCH CHI DƯỚI TẠI BỆNH VIỆN ĐẠI HỌC Y HÀ NỘI
Authors: Trần Đức Thịnh , Nguyễn Thị Phương , Đỗ Thế Quyền , Nguyễn Duy Gia , Nguyễn Duy Thắng , Vũ Ngọc Tú
Published: January 2024

Mục tiêu nghiên cứu: Đánh giá kết quả sớm của phương pháp phẫu thuật bắc cầu điểu trị Bệnh động mạch chi dưới (BĐMCD) tại Bệnh viện Đại học Y Hà Nội từ tháng 1/2019 đến tháng 7/2023. Đối tượng và phương pháp nghiên cứu: Nghiên cứu mô tả cắt ngang, hồi cứu. Tất cả các bệnh nhân được chẩn đoán là BĐMCD được chỉ định điều trị bằng phương pháp phẫu thuật bắc cầu động mạch tại bệnh viện đại học Y Hà Nội từ tháng 1/2019 đến tháng 7/2023. Kết quả: Có 53 bệnh nhân trong nghiên cứu, có tuổi trung bình là 65.07 ± 8,1 tuổi. Bệnh nhân nam chiếm 87.5%. Bệnh nhân đến viện với triệu chứng đau cách hồi tương ứng với giai đoạn IIB là 26.8%, giai đoạn III là 41.1%, giai đoạn IV là 32,1%. Kèm theo nhiều bệnh lý nền như tăng huyết áp (85.7%), đái tháo đường (48.2%), tai biến mạch máu não cũ (10.7%). Tổn thương trên cận lâm sàng nhiều nhất ở tầng đùi khoeo (66.07%). Phẫu thuật tiến hành nhiều nhất là bắc cầu đùi - khoeo trên gối (41,1%). Vật liệu sử dụng làm cầu nối là Tĩnh mạch hiển (57.14%), mạch nhân tạo (42.86%). Thời gian phẫu thuật trung bình 143,04 ± 45,04 phút. Không trường hợp nào có biến chứng trong mổ. Thời gian nằm viện sau mổ trung bình là 10.41±7,11 ngày. Tỷ lệ biến chứng sớm sau mổ là 19.64%, phải mổ lại sớm là 12.5%. Không có bệnh nhân nào tử vong trong quá trình điều trị. Kết luận: Phẫu thuật bắc cầu hiện nay là phương pháp an toàn, mang lại hiệu quả cao trong điều trị Bệnh động mạch chi dưới tại Bệnh viện Đại học Y Hà nội.
Expert System for Diagnosing Airborne Diseases Airborne Diseases in Humans with Fuzzy Logic Mamdani Method
Authors: Subekti Dayat , Priyanto Agung , Mukti Agung Permana , Azzuhry Dimas Rully
Published: January 2024

People in today's information age are increasingly demanding all kinds of information to be presented quickly. One of them is information in the medical field, in this case information about the diagnosis of a disease which is usually in the form of an expert system. Various methods are used in expert systems, one of which is Mamdani fuzzy logic or better known as the Mamdani inference system. The use of fuzzy logic in diagnosing this disease does not require numbers as input.

The Mamdani fuzzy logic expert system in this study is specifically used to diagnose infectious diseases in humans, namely: whooping cough or pertussis, measles, diphtheria, mumps, meningitis, tuberculosis, variola, and varicella. This system diagnoses diseases based on inputting the characteristics or symptoms suffered. The diagnosis result is a number of possible diagnoses for each disease. The closer the number to 1 (one), the higher the probability of contracting one of the diseases mentioned above.

The system that has been created can already be used to detect diseases according to the characteristics or symptoms entered. However, this system is only a research and the results cannot be used as a reference for real disease diagnosis.

 
AI-DRIVEN ENVIRONMENTAL HEALTH DISEASE MODELING: A REVIEW OF TECHNIQUES AND THEIR IMPACT ON PUBLIC HEALTH IN THE USA AND AFRICAN CONTEXTS
Authors: Nzubechukwu Chukwudum Ohalete  , Oluwatoyin Ayo-Farai  , Tolulope O Olorunsogo  , Paschal Maduka  , Temidayo Olorunsogo
Published: January 2024

This scholarly paper embarks on an exploratory journey into the realm of AI-driven environmental health disease modeling, with a keen focus on its implications in the diverse healthcare landscapes of the USA and Africa. The study's background delves into the historical evolution of disease modeling techniques, emphasizing the revolutionary role of AI in modern public health strategies. It meticulously examines the comparative effectiveness of AI models in these distinct regions, addressing the challenges and opportunities inherent in AI-driven health models. Aiming to unravel the multifaceted impact of AI in disease prediction and public health policy, the paper navigates through various thematic corridors. It critically analyzes the significance of data sources and quality, ethical considerations in AI health modeling, and the integration of AI models into public health policies. The scope of the paper encompasses a comprehensive review of AI's efficacy in predicting environmental diseases, its role in enhancing disease surveillance systems, and the geographic and socioeconomic variations affecting model accuracy. The main findings reveal that AI models, while effective in disease prediction and surveillance, encounter challenges related to data integrity and ethical complexities. The study concludes that the integration of AI in healthcare necessitates a balanced approach, advocating for policies that support the development of context-specific AI models and address ethical concerns. Recommendations include fostering interdisciplinary collaboration and continuous evaluation of AI models to align them with evolving healthcare needs and ethical standards. This paper serves as a beacon for understanding AI's transformative potential in environmental health disease modeling, offering insights that are crucial for shaping future public health strategies and interventions.

Keywords:  AI in Healthcare, Disease Modeling, Public Health Policy, Data Quality, Ethical Considerations, Geographic Variations.
THE RISE OF NON-COMMUNICABLE DISEASES: A GLOBAL HEALTH REVIEW OF CHALLENGES AND PREVENTION STRATEGIES
Authors: Olufunke Omotayo  , Chinedu Paschal Maduka  , Muridzo Muonde  , Tolulope O Olorunsogo  , Jane Osareme Ogugua
Published: January 2024

Non-communicable diseases (NCDs) have emerged as a major global health concern, posing significant challenges to healthcare systems worldwide. This paper provides a succinct overview of the rising prevalence of NCDs, examining the key challenges faced and outlining preventive strategies adopted on a global scale. The escalation of NCDs, including cardiovascular diseases, diabetes, cancer, and respiratory conditions, is intricately linked to lifestyle factors, urbanization, and demographic transitions. This paper explores the multifaceted challenges posed by the growing burden of NCDs, encompassing economic implications, disparities in healthcare access, and the complex interplay of genetic and environmental factors. Addressing NCDs necessitates a comprehensive and multi-sectoral approach. Prevention strategies are crucial components of global health initiatives, encompassing lifestyle modifications, public health interventions, and policy frameworks. This paper outlines successful preventive measures adopted globally, including tobacco control initiatives, promotion of healthy diets and physical activity, and the integration of early screening and detection programs. Despite notable progress, several challenges persist, requiring sustained efforts to curb the NCD epidemic. Issues such as limited resources, inadequate healthcare infrastructure, and the need for cross-sectoral collaboration are explored in this paper. Additionally, the role of education, awareness campaigns, and technological innovations in shaping preventive strategies is emphasized. This paper provides a concise yet comprehensive overview of the global challenges posed by the rise of NCDs and highlights effective prevention strategies. It underscores the urgency for continued collaborative efforts to mitigate the impact of NCDs on global health, with a focus on fostering healthier lifestyles, improving healthcare access, and implementing evidence-based policies.

Keywords:  Non-communicable Diseases, Global Health, Prevention, Disease, Review, NCD.
Real-time pollen identification using holographic imaging and fluorescence measurements
Authors: Erb Sophie , Graf Elias , Zeder Yanick , Lionetti Simone , Berne Alexis , Clot Bernard , Lieberherr Gian , Tummon Fiona , Wullschleger Pascal , Crouzy Benoît
Published: January 2024

Abstract. Over the past few years, a diverse range of automatic real-time instruments has been developed to respond to the needs of end users in terms of information about atmospheric bioaerosols. One of them, the SwisensPoleno Jupiter, is an airflow cytometer used for operational automatic bioaerosol monitoring. The instrument records holographic images and fluorescence information for single aerosol particles, which can be used for identification of several aerosol types, in particular different pollen taxa. To improve the pollen identification algorithm applied to the SwisensPoleno Jupiter and currently based only on the holography data, we explore the impact of merging fluorescence spectra measurements with holographic images. We demonstrate, using measurements of aerosolised pollen, that combining information from these two sources results in a considerable improvement in the classification performance compared to using only a single source (balanced accuracy of 0.992 vs. 0.968 and 0.878). This increase in performance can be ascribed to the fact that often classes which are difficult to resolve using holography alone can be well identified using fluorescence and vice versa. We also present a detailed statistical analysis of the features of the pollen grains that are measured and provide a robust, physically based insight into the algorithm's identification process. The results are expected to have a direct impact on operational pollen identification models, particularly improving the recognition of taxa responsible for respiratory allergies.
Decomposition of skill scores for conditional verification – Impact of AMO phases on the predictability of decadal temperature forecasts
Authors: Richling Andy , Grieger Jens , Rust Henning W.
Published: January 2024

Abstract. We present a decomposition of skill scores for the conditional verification of weather and climate forecast systems. Aim is to evaluate the performance of such a system individually for predefined subsets with respect to the overall performance. The overall skill score is decomposed into: (1) the subset skill score assessing the performance of a forecast system compared to a reference system for a particular subset; (2) the frequency weighting accounting for varying subset size; (3) the reference weighting relating the performance of the reference system in the individual subsets to the performance of the full data set. The decomposition and its interpretation is exemplified using a synthetic data set. Subsequently we use it for a practical example from the field of decadal climate prediction: An evaluation of the Atlantic-European near-surface temperature forecast from the German initiative Mittelfristige Klimaprognosen (MiKlip) decadal prediction system conditional on different Atlantic Meridional Oscillation (AMO) phases during initialization. With respect to the chosen Western European North Atlantic sector, the decadal prediction system preop-dcpp-HR performs better than the un-initialized simulations mostly due to performance gain during a positive AMO phase. Compared to the predecessor system (preop-LR), no overall performance benefits are made in this region, but positive contributions are achieved for initialization in neutral AMO phases. Additionally, the decomposition reveals a strong imbalance among the subsets (defined by AMO phases) in terms of reference weighting allowing for sophisticated interpretation and conclusions. This skill score decomposition framework for conditional verification is a valuable tool to analyze the effect of physical processes on forecast performance and consequently supports model development and improvement of operational forecasts.
Advancing Arctic sea ice remote sensing with AI and deep learning: now and future
Authors: Li Wenwen , Hsu Chia-Yu , Tedesco Marco
Published: January 2024

Abstract. The revolutionary advances of Artificial Intelligence (AI) in the past decade have brought transformative innovation across science and engineering disciplines. Also in the field of Arctic science, we have witnessed an increasing trend in the adoption of AI, especially deep learning, to support the analysis of Arctic big data and facilitate new discoveries. In this paper, we provide a comprehensive review of the applications of deep learning in sea ice remote sensing domains, focusing on problems such as sea ice lead detection, thickness estimation, concentration, sea ice extent forecasting and motion detection as well as sea ice type classification. In addition to discussing these applications, we also summarize technological advances that provide customized deep learning solutions, including new loss functions and learning strategies to better understand sea ice dynamics. To promote the growth of this exciting interdisciplinary field, we further explore several research areas where the Arctic sea ice community can benefit from cutting-edge AI technology. These areas include improving multi-modal deep learning capabilities, enhancing model accuracy in measuring prediction uncertainty, better leveraging AI foundation models, and deepening the integration with physics-based models. We hope that this paper can serve as a cornerstone in the progress of Arctic sea ice research using AI and inspire further advances in this field.
How Rainfall Events Modify Trace Gas Concentrations in Central Amazonia
Authors: Machado Luiz A. T. , Kesselmeier Jürgen , Botia Santiago , Van Asperen Hella , de Araújo Alessandro C. , Artaxo Paulo , Edtbauer Achim , Ferreira Rosa , Harder Hartwig , Jones Sam , Dias-Júnior Cléo Q. , Haytzmann Guido G. , Quesada Carlos A. , Komiya Shujiro , Lavric Jost , Lelieveld Jos , Levin Ingeborg , Nölscher Anke , Pfannerstill Eva , Pöhlker Mira , Pöschl Ulrich , Ringsdorf Akima , Rizzo Luciana , Yáñez-Serrano Ana M. , Trumbore Susan , Valenti Wanda I. D. , Vila-Guerau de Arellano Jordi , Walter David , Williams Jonathan , Wolff Stefan , Pöhlker Christopher
Published: January 2024

Abstract. This study investigates the rain-initiated mixing and variability in the concentration of selected trace gases in the atmosphere over the central Amazon rain forest. It builds on comprehensive data from the Amazon Tall Tower Observatory (ATTO), spanning from 2013 to 2020 and comprising the greenhouse gases (GHG) carbon dioxide (CO2) and methane (CH4), the reactive trace gases carbon monoxide (CO), ozone (O3), nitric oxide (NO), and nitrogen dioxide NO2 (NO2) as well as selected volatile organic compounds (VOC). Based on more than 1000 analyzed rainfall incidents, the study resolves the trace gas concentration patterns before, during, and after the rain events, along with its vertical concentration gradients across the forest canopy. The assessment of the rainfall events was conducted independently for daytime and nighttime periods, which allows us to elucidate the inﬂuence of solar radiation. The concentrations of CO2, CO, and CH4 clearly declined during rainfall, which can be attributed to the downdraft-related entrainment of pristine air from higher altitudes into the boundary layer, a reduction of the photosynthetic activity under increased cloud cover, as well as changes in the surface ﬂuxes. Notably, CO showed a faster reduction than CO2, and the vertical gradient of CO2 and CO is steeper than for CH4. Conversely, the O3 concentration increased across all measurement heights in the course of the rain-related downdrafts. Following the O3 enhancement by up to a factor of two, NO and VOC concentrations decreased, whereas NO2 increased. The temporal and vertical variability of the trace gases is intricately linked to the diverse sink and source processes, surface ﬂuxes, and free troposphere transport. Within the canopy, several interactions unfold among soil, atmosphere, and plants, shaping the overall dynamics. Also, the concentration of biogenic VOC (BVOC) clearly varied with rainfall, driven by factors such as light, temperature, physical transport, and soil processes. Our results disentangle the patterns in trace gas concentration in the course of the sudden and vigorous atmospheric mixing during rainfall events. By selectively uncovering processes that are not clearly detectable under undisturbed conditions, our results contribute to a better understanding of the trace gas life cycle and its interplay with meteorology, cloud dynamics, and rainfall in the Amazon and beyond.
Exploring climate stabilisation at different global warming levels in ACCESS-ESM-1.5
Authors: King Andrew D. , Ziehn Tilo , Chamberlain Matthew , Borowiak Alexander R. , Brown Josephine R. , Cassidy Liam , Dittus Andrea J. , Grose Michael , Maher Nicola , Paik Seungmok , Perkins-Kirkpatrick Sarah E. , Sengupta Aditya
Published: January 2024

Abstract. Under the Paris Agreement, signatory nations aim to keep global warming well below 2 °C above pre-industrial levels and preferably below 1.5 °C. This implicitly requires achieving net-zero or net-negative greenhouse gas emissions to ensure long-term global temperature stabilisation or reduction. Despite this requirement, there have been few analyses of stabilised climates and there is a lack of model experiments to address our need for understanding the implications of the Paris Agreement. Here, we describe a new set of experiments using the Australian Community Climate and Earth System Simulator earth system model (ACCESS-ESM-1.5) that enables analysis of climate evolution under net-zero emissions, and we present initial results. Seven 1000-year long simulations were run with global temperatures stabilising at levels in line with the Paris Agreement and at a range of higher global warming levels. We provide an overview of the experimental design and use these simulations to demonstrate the consequences of delayed attainment of global net-zero carbon dioxide emissions. As the climate stabilises under net-zero emissions, we identify significant and robust changes in temperature and precipitation patterns including continued Southern Ocean warming and reversal of transient mid-latitude drying trends. Regional climate changes under net-zero emissions differ greatly including contrasting trajectories of sea ice extent between the Arctic and Antarctic. We also examine the El Niño-Southern Oscillation (ENSO) and find evidence of reduced amplitude and frequency of ENSO events under climate stabilisation relative to projections under transient warming. An analysis at specific global warming levels shows significant regional changes continue for centuries after emissions cessation. Our findings suggest substantial long-term climate changes are possible even under net-zero emissions pathways. We hope these simulations will be of use to the community and hopefully motivate further experiments and analyses based on other earth system models.
Impact of rain-on-snow events on snowpack structure and runoff under a boreal canopy
Authors: Bouchard Benjamin , Nadeau Daniel F. , Domine Florent , Wever Nander , Michel Adrien , Lehning Michael , Isabelle Pierre-Erik
Published: January 2024

Abstract. Rain-on-snow events can cause severe flooding in snow–dominated regions. These are expected to become more frequent in the future as climate change shifts the precipitation from snowfall to rainfall. However, little is known about how winter rainfall interacts with an evergreen canopy and affects the underlying snowpack. In this study, we document 5 years of rain-on-snow events and snowpack observations at two boreal forested sites of eastern Canada. Our observations show that rain-on-snow events over a boreal canopy leads to the formation of melt–freeze layers as rainwater refreezes at the surface of the sub–canopy snowpack. They also generate frozen percolation channels, suggesting that preferential flow is favored in the sub–canopy snowpack during rain-on-snow events. We then used the multi–layer snow model SNOWPACK to simulate the sub–canopy snowpack at both sites. Although SNOWPACK performs reasonably well in reproducing snow height (RMSE = 17.3 cm), snow surface temperature (RMSE = 1.0 °C), and density profiles (agreement score = 0.79), its performance declines when it comes to simulating snowpack stratigraphy, as it fails to reproduce many of the observed melt–freeze layers. To correct for this, we implemented a densification function of the intercepted snow in the canopy module of SNOWPACK. This new feature allows 27 of the 32 observed melt–freeze layers induced by rain-on-snow events to be formed by the model, instead of only 18 with the original canopy module. This new model development also delays and reduces the snowpack runoff. Indeed, it triggers the unloading of dense unloaded snow layers with small rounded grains, which in turn produces fine–over–coarse transitions that limit percolation and favor refreezing. Our results show that the boreal vegetation modulates the sub–canopy snowpack structure and runoff from rain-on-snow events. Overall, this study highlights the need for canopy snow properties measurements to improve hydrological models in forested snow–covered regions.
Benchmarking of SWE products based on outcomes of the SnowPEx+ Intercomparison Project
Authors: Mudryk Lawrence , Mortimer Colleen , Derksen Chris , Elias Chereque Aleksandra , Kushner Paul
Published: January 2024

Abstract. We assess and rank 23 gridded snow water equivalent (SWE) products by implementing a novel evaluation strategy using a new suite of reference data from two cross-validated sources and a series of product inter-comparisons. The new reference data combines in situ measurements from both snow courses and airborne gamma measurements. Compared to previous evaluations of gridded products, we have substantially increased the spatial coverage and sample size across North America, and we are able to evaluate product performance across both mountain and non-mountain regions. The evaluation strategy we use ranks overall relative product performance while still accounting for individual differences in ability to represent SWE climatology, variability, and trends. Assessing these gridded products fills an important gap in the literature since individual gridded products are frequently chosen without prior justification as the basis for evaluating land surface and climate model outputs, along with other climate applications. The top performing products across the range of tests performed are ERA5-Land followed by the Crocus snow model. Our evaluation indicates that accurate representation of hemispheric SWE varies tremendously across the range of products. While most products are able to represent SWE reasonably well across Northern Hemisphere non-mountainous regions, the ability to accurately represent SWE in mountain regions and to accurately represent historical trends are much more variable. Finally, we demonstrate that for the ensemble of products evaluated here, attempts to assimilate surface snow observations and/or satellite measurements lead to a deleterious influence on regional snow mass trends, which is an important consideration for how such gridded products are produced and applied in the future.
On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results
Authors: Abramowitz Gab , Ukkola Anna , Hobeichi Sanaa , Cranko Page Jon , Lipson Mathew , De Kauwe Martin , Green Sam , Brenner Claire , Frame Jonathan , Nearing Grey , Clark Martyn , Best Martin , Anthoni Peter , Arduini Gabriele , Boussetta Souhail , Caldararu Silvia , Cho Kyeungwoo , Cuntz Matthias , Fairbairn David , Ferguson Craig , Kim Hyungjun , Kim Yeonjoo , Knauer Jürgen , Lawrence David , Luo Xiangzhong , Malyshev Sergey , Nitta Tomoko , Ogee Jerome , Oleson Keith , Ottlé Catherine , Peylin Phillipe , de Rosnay Patricia , Rumbold Heather , Su Bob , Vuichard Nicolas , Walker Anthony , Wang-Faivre Xiaoni , Wang Yunfei , Zeng Yijian
Published: January 2024

Abstract. Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles, both in future climate projections and weather forecasts. We describe a Model Intercomparison Project (MIP) that compares the surface turbulent heat flux predictions of around 20 different land models provided with in-situ meteorological forcing, evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance based flux tower sites. Several out-of-sample empirical model predictions of site fluxes are used as benchmarks to quantify the degree to which land model performance could improve across a broad range of metrics. The performance discrepancy between empirical and mechanistic model predictions also provides a potential pathway to understand sources of model error. Sites with unusual behaviour, complicated processes, poor data quality or uncommon flux magnitude will be more difficult to predict for both mechanistic and empirical models. Results suggest that latent heat flux and net ecosystem exchange of CO2 are better predicted by land models than sensible heat flux, which at least conceptually would appear to have fewer physical processes controlling it. Land models that are implemented in Earth System Models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. Flux tower data quality is also explored as an uncertainty source, with the difference between energy-balance corrected versus raw fluxes examined, as well as filtering for low wind speed periods. Land model performance does not appear to improve with energy-balance corrected data, and indeed some results raised questions about whether the correction process itself was appropriate. In both cases results were broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. The PLUMBER2 approach, and its openly available data, enable precise isolation of the locations and conditions in which model developers can know that a given land model can improve, allowing information pathways and discrete parametrisations in models to be identified and targeted for model development.
Technical note: An open-source, low-cost system for continuous nitrate monitoring in soil and open water
Authors: Bulusu Sahiti , Prieto García Cristina , Dahlke Helen E. , Levintal Elad
Published: January 2024

Abstract. Nitrate (NO3-), mainly leaching with soil pore water, is the primary nonpoint source pollutant of groundwater worldwide. Obtaining real-time information on nitrate levels in soils would allow gaining a better understanding of the sources and transport dynamics of nitrate through the unsaturated zone. However, conventional nitrate detection techniques (e.g. soil sample analysis) necessitate costly, laboratory-grade equipment for analysis, along with human resources, resulting in a laborious and time-intensive procedure. These drawbacks raise the need to develop cost-effective and automated systems for in situ nitrate measurements in field conditions. This study presents the development of a low-cost, portable, automated system for field measurements of nitrate in soil pore water and open water bodies. The system is based on the spectrophotometric determination of nitrate using a single reagent. The system design and processing software are openly accessible, including a building guide, to allow duplicating or changing the system according to user-specific needs. Three field tests, conducted over five weeks, validated the system’s measurement capabilities within the range of 0–10 ppm NO3--N with a low RMSE of <0.2 ppm NO3--N when comparing the results to standard laboratory nitrate analysis. Data derived from such a system allow tracking of the temporal variation in soil nitrate, thus opening new possibilities for diverse soil and nutrient management studies.
A comprehensive assessment of electrochemical ocean alkalinity enhancement in seawater: kinetics, efficiency, and precipitation thresholds
Authors: Ringham Mallory , Hirtle Nathan , Shaw Cody , Lu Xi , Herndon Julian , Carter Brendan , Eisaman Matthew
Published: January 2024

Abstract. Ocean alkalinity enhancement (OAE) is a promising approach to marine carbon dioxide removal (mCDR) that leverages the large surface area and carbon storage capacity of the oceans to sequester atmospheric CO2 as dissolved bicarbonate (HCO3-). The SEA MATE (Safe Elevation of Alkalinity for the Mitigation of Acidification Through Electrochemistry) process uses electrochemistry to convert some of the salt (NaCl) in seawater or brine into aqueous acid (HCl), which is removed from the system, and base (NaOH), which is returned to the ocean with the remaining seawater. The resulting increase in seawater pH and alkalinity causes a shift in dissolved inorganic carbon (DIC) speciation toward carbonate and a decrease in the surface-ocean pCO2. The shift in the pCO­2 results in enhanced CO2 uptake or reduced CO2 loss by the seawater due to gas exchange. The net result of this process is the increase of surface-ocean DIC, where it is durably stored as mostly bicarbonate and some carbonate. In this study, we systematically test the efficiency of CO2 uptake in seawater treated with NaOH at beaker (1 L), aquaria (15 L), and tank (6000 L) scales to establish operational boundaries for safety and efficiency in scaling up to field experiments. Preliminary results show CO2 equilibration occurred on order of weeks to months, depending on circulation, air forcing, and air bubbling conditions within the test tanks. An increase of ~0.7–0.9 mol DIC/ mol added alkalinity (in the form of NaOH) was observed through analysis of seawater bottle samples and pH sensor data, consistent with the value expected given the values of the carbonate system equilibrium calculations for the range of salinities and temperatures tested. Mineral precipitation occurred when the bulk seawater pH exceeded 10.0 and Ωaragonite exceeded 30.0. This precipitation was dominated by Mg(OH)2 over hours to 1 day before shifting to CaCO3, aragonite precipitation. These data, combined with models of the dilution and advection of alkaline plumes, will allow for estimation of the amount of carbon dioxide removal expected from OAE pilot studies. Future experiments should better approximate field conditions including sediment interactions, biological activity, ocean circulation, air-sea gas exchange rates, and mixing-zone dynamics.
Global impact of benthic denitrification on marine N2 fixation and primary production simulated by a variable-stoichiometry Earth system model
Authors: Li Na , Somes Christopher J. , Landolfi Angela , Chien Chia-Te , Pahlow Markus , Oschlies Andreas
Published: January 2024

Abstract. Nitrogen (N) is a crucial limiting nutrient for phytoplankton growth in the ocean. The main source of bioavailable N in the ocean is delivered by N2-fixing diazotrophs in the surface layer. Since field observation of N2 fixation are spatially and temporally sparse, the fundamental processes and mechanisms controlling N2 fixation are not well understood and constrained. Here, we implement benthic denitrification in an Earth System Model of intermediate complexity (UVic-ESCM 2.9) coupled to an optimality-based plankton ecosystem model (OPEM v1.1). Benthic denitrification occurs mostly in coastal upwelling regions and on shallow continental shelves, and is the largest N-loss process in the global ocean. We calibrate our model against three different combinations of observed Chl, NO3-, PO43-, O2 and N* = NO3- −16PO43- +2.9. The inclusion of N* provides a powerful constraint on biogeochemical model behavior. Our new model version including benthic denitrification simulates higher global rates of N2 fixation with a more realistic distribution extending to higher latitudes that are supported by independent estimates based on geochemical data. Oxygen deficient zone volume and water column denitrification rates are reduced in the new version, indicating that including benthic denitrification may improve global biogeochemical models that commonly overestimate anoxic zones. With the improved representation of the ocean N cycle, our new model configuration also yields better global net primary production (NPP) when compared to the independent datasets not included in the calibration. Benthic denitrification plays an important role shaping N2 fixation and NPP throughout the global ocean in our model, and should be considered when evaluating and predicting their response to environmental change.
An assessment of equatorial Atlantic interannual variability in OMIP simulations
Authors: Prigent Arthur , Farneti Riccardo
Published: January 2024

Abstract. The eastern equatorial Atlantic (EEA) seasonal cycle and interannual variability strongly influence the climate of the surrounding continents. It is thus crucial that models used in both climate predictions and future climate projections are able to simulate them accurately. In that context, the EEA seasonal cycle and interannual variability are evaluated over the period 1985–2004 in models participating to the Ocean Model Intercomparison Project Phases 1 and 2 (OMIP1 and OMIP2). The main difference between OMIP1 and OMIP2 simulations is their atmospheric forcing: CORE-II and JRA55-do, respectively. Seasonal cycles of the equatorial Atlantic zonal winds, sea level anomaly and sea surface temperature in OMIP1 and OMIP2 are comparable to reanalysis datasets. Yet, some discrepancies exist in both OMIP ensembles: the thermocline is too diffusive and there is a lack of cooling during the development of the Atlantic cold tongue. In addition, the vertical ocean velocity in the eastern equatorial Atlantic in boreal summer is larger in OMIP1 than in OMIP2 simulations. The EEA interannual sea surface temperature variability in the OMIP1 ensemble mean is found to be 51 % larger (0.62 ± 0.04 °C) than the OMIP2 ensemble mean (0.41 ± 0.03 °C). Sensitivity experiments demonstrate that the discrepancy in interannual sea surface temperature variability between OMIP1 and OMIP2 is mainly attributed to their wind forcing. While the April-May-June zonal wind variability in the western equatorial Atlantic is similar in both forcing, the zonal wind variability peaks in April for JRA55-do and in May for CORE-II. Differences in surface heat fluxes between the atmospheric forcing datasets have no significant impacts on the simulated interannual SST variability.
Ice flow dynamics of the northwestern Laurentide Ice Sheet during the last deglaciation
Authors: Stoker Benjamin J. , Dulfer Helen E. , Stokes Chris R. , Brown Victoria H. , Clark Christopher D. , Ó Cofaigh Colm , Evans David J. A. , Froese Duane , Norris Sophie L. , Margold Martin
Published: January 2024

Abstract. Reconstructions of palaeo-ice stream activity provide an insight into the processes governing ice stream evolution over millennial timescales. The northwestern sector of the Laurentide Ice Sheet experienced a period of rapid retreat driven by warming during the Bølling–Allerød (14.7 – 12.9 ka) which may have contributed significantly to global mean sea level rise during this time. It therefore provides an opportunity to investigate ice sheet dynamics during a phase of rapid ice sheet retreat. Here, we classify coherent groups of ice flow parallel lineations into 326 flowsets and then categorise them as ice stream, deglacial, inferred deglacial or event type flowsets. Combined with ice marginal landforms and a new ice margin chronology (Dalton et al., 2023), we present the first reconstruction of ice flow dynamics of the northwestern Laurentide Ice Sheet at 500-year timesteps through the last deglaciation. At the local Last Glacial Maximum (17.5 ka), the ice stream network was dominated by large, marine-terminating ice streams (>1000 km long) that were fed by the Laurentide-Cordilleran ice saddle to the south and the Keewatin Ice Dome to the east. As the ice margin retreated onshore, the drainage network was characterised by shorter, land-terminating ice streams (<200 km long), with the exception of the Bear Lake and Great Slave Lake ice streams (~600 km long) that terminated in large glacial lakes. Rapid reorganisation of the ice drainage network, from predominantly northerly ice flow to westerly ice flow, occurred over ~2000 years, coinciding with a period of rapid ice sheet surface lowering in the ice saddle region. We note a peak in ice stream activity during the Bølling–Allerød that we suggest is a result of increased ablation and a steepening of the ice surface slope in ice stream onset zones and the increase in driving stresses which contributed to rapid ice drawdown. The subsequent cessation of ice stream activity by the end of the Bølling–Allerød was a result of ice drawdown lowering the ice surface profile, reducing driving stresses and leading to widespread ice stream shut-down.
Characterization of aerosol over the Eastern Mediterranean by polarization sensitive Raman lidar measurements during A-LIFE – aerosol type classification and type separation
Authors: Groß Silke , Freudenthaler Volker , Haarig Moritz , Ansmann Albert , Toledano Carlos , Mateos David , Seibert Petra , Mamouri Rodanthi-Elisavet , Nisantzi Argyro , Gasteiger Josef , Dollner Maximilian , Tipka Anne , Schöberl Manuel , Teri Marilena , Weinzierl Bernadett
Published: January 2024

Abstract. Aerosols are key players in the Earth’s climate system with mineral dust being one major component of the atmospheric aerosol load. While former campaigns focused on investigating the properties and effects of rather pure mineral dust layers, the A-LIFE (Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics) campaign in April 2017 aimed to characterize dust in complex aerosol mixtures. In this study we present ground-based lidar measurements that were performed at Limassol, Cyprus, in April 2017. During our measurement period, the measurement site was affected by complex mixtures of dust from different sources and pollution aerosols from local sources as well as long-range transported. We found mean values of the particle linear depolarization ratio and extinction-to-backscatter ratio (lidar ratio) of 0.27 ± 0.02 and 41 sr ± 5 sr at 355 nm and of 0.30 ± 0.02 and 39 sr ± 5 sr at 532 nm for Arabian dust, and of 0.27 ± 0.02 and 55 sr ± 8 sr at 355 nm and of 0.28 ± 0.02 and 53 sr ± 7 sr at 532 nm for Saharan dust. The values found for pollution aerosols of the particle linear depolarization ratio and the lidar ratio are 0.05 ± 0.02 at 355 nm and 0.04 ± 0.02 at 532 nm, and 65 sr ± 12 sr at 355 nm and 60 sr ± 16 sr at 532 nm, respectively. We use our measurements for aerosol typing and compare that to aerosol typing from sun photometer data, in-situ measurements and trajectory analysis. The different methods agree well for the derived aerosol type, but looking at the derived dust mass concentration from different methods, the trajectory analysis frequently underestimate high dust concentration that were found in major mineral dust events.
Fire Weather Compromises Forestation-reliant Climate Mitigation Pathways
Authors: Jäger Felix , Schwaab Jonas , Quilcaille Yann , Windisch Michael , Doelman Jonathan , Frank Stefan , Gusti Mykola , Havlik Petr , Humpenöder Florian , Lessa Derci Augustynczik Andrey , Müller Christoph , Narayan Kanishka B. , Padrón Ryan S. , Popp Alexander , van Vuuren Detlef , Wögerer Michael , Seneviratne Sonia I.
Published: January 2024

Abstract. Forestation can contribute to climate change mitigation. However, an increasing frequency and intensity of climate extremes is posed to have profound impact on forests, and consequently on the mitigation potential of forestation efforts. In this perspective, we critically assess forestation-reliant climate mitigation scenarios from five different Integrated Assessment Models (IAMs) by show-casing the spatially explicit exposure of forests to fire weather and the simulated increase in global annual burned area. We provide a detailed description of the feedback from climate change to forest carbon uptake in IAMs. Few IAMs are currently accounting for feedback mechanisms like loss from fire disturbance. Consequently many forestation areas proposed by IAM scenarios will be exposed to fire-promoting weather conditions and without costly prevention measures might be object to frequent burning. We conclude that the actual climate mitigation portfolio in IAM scenarios is subject to substantial uncertainty and that the risk of overly optimistic estimates of negative emission potential of forestation should be avoided. As a way forward we propose how to integrate more detailed climate information when modeling climate mitigation pathways heavily relying on forestation.
Seafloor sediment characterization to improve estimate of organic carbon standing stocks in continental shelves
Authors: Brenan Catherine , Kienast Markus , Maselli Vittorio , Algar Christopher , Misiuk Benjamin , Brown Craig J.
Published: January 2024

Abstract. Continental shelf sediments contain some of the largest stocks of organic carbon (OC) on Earth and play a vital role in influencing the global carbon cycle. Quantifying how much OC is stored in shelf sediments and determining its residence time is key to assessing how human activities can accelerate the process of OC remineralization into carbon dioxide. Spatial variations in terrestrial carbon stocks are well studied and mapped at high resolution, but our knowledge of the distribution of marine OC in different seafloor settings is still very limited, particularly in the highly dynamic and spatially variable shelf environments. The lack of knowledge reduces our ability to understand and predict how much and for how long oceans sequester CO2. In this study, we use high-resolution multibeam echosounder (MBES) data from the Eastern Shore Islands offshore Nova Scotia (Canada), combined with OC measurements from discrete samples, to assess the distribution of OC content in seafloor sediments. We derive three different spatial estimates of organic carbon: i) assuming a homogenous seafloor the carbon stock estimates were scaled to the entire study region; ii) using a high-resolution substrate map, the estimates were scaled to the areas of soft substrate only, and, finally, iii) using Empirical Bayesian Regression Kriging (EBRK) regression prediction within the area of soft substrate, carbon stock estimates in areas of soft substrate were refined to account for spatial variability in the concentration of OC. These three distinct spatial models yielded dramatically different estimates of average standing stock of OC in our study area, 1275, 259 and 203 Mt of OC respectively. Our study demonstrates that high-resolution mapping is critically important for improved estimates of OC stocks on continental shelves, and to the identification of carbon hotspots that need to be considered in seabed management and climate mitigation strategies.
Salinity and Stratification at the Sea Ice Edge (SASSIE): An oceanographic field campaign in the Beaufort Sea
Authors: Drushka Kyla , Westbrook Elizabeth , Bingham Frederick , Gaube Peter , Dickinson Suzanne , Fournier Severine , Menezes Viviane , Misra Sidharth , Perez Jaynise , Rainville Edwin J. , Schanze Julian , Schmidgall Carlyn , Shcherbina Andrey , Steele Michael , Thomson Jim , Zippel Seth
Published: January 2024

Abstract. As our planet warms, Arctic sea ice coverage continues to decline resulting in complex feedbacks with the climate system. The core objective of NASA’s Salinity and Stratification at the Sea Ice Edge (SASSIE) mission is to understand how ocean salinity and near-surface stratification affect upper ocean heat content, and thus sea ice freeze and melt. SASSIE specifically focuses on the formation of Arctic sea ice in autumn. The SASSIE field campaign in 2022 collected detailed observations of upper ocean properties and meteorology near the sea ice edge in the Beaufort Sea using ship-based and piloted and drifting assets. The observations collected during SASSIE include vertical profiles of stratification up to the sea surface, air-sea fluxes, and ancillary measurements that are being used to better understand the role of salinity in coupled Arctic air-sea-ice processes. This publication provides a detailed overview of the activities during the 2022 SASSIE campaign and presents the publicly available datasets generated by this mission, inducing an accompanying repository that highlights the numerical routines used to generate the figures shown in this manuscript.
European soil bulk density and organic carbon stock database using machine learning based pedotransfer function
Authors: Chen Songchao , Chen Zhongxing , Zhang Xianglin , Luo Zhongkui , Schillaci Calogero , Arrouays Dominique , Richer-de-Forges Anne , Shi Zhou
Published: January 2024

Abstract. Soil bulk density (BD) serves as a fundamental indicator of soil health and quality, exerting a significant influence on critical factors such as plant growth, nutrient availability, and water retention. Due to its limited availability in soil databases, the application of pedotransfer functions (PTFs) has emerged as a potent tool for predicting BD using other easily measurable soil properties, while the impact of these PTFs’ accuracy on soil organic carbon (SOC) stock calculation has been rarely explored. In this study, we proposed an innovative local modelling approach for predicting BD across Europe using the recently released BD data from the LUCAS Soil 2018 (0–20 cm). Our approach involved a combination of neighbour sample search, Forward Recursive Feature Selection (FRFS) and Random Forest (RF) model (local-RFFRFS). The results showed that local-RFFRFS had a good performance in predicting BD (R2 of 0.58, RMSE of 0.19 g cm-3), surpassing the traditional PTFs (R2 of 0.40–0.45, RMSE of 0.22 g cm-3) and global PTFs using RF with and without FRFS (R2 of 0.56–0.57, RMSE of 0.19 g cm-3). Interestingly, we found the best traditional PTF (R2=0.84, RMSE=1.39 kg m-2) performed close to the local-RFFRFS (R2=0.85, RMSE=1.32 kg m-2) in SOC stock calculation using BD predictions. However, the local-RFFRFS still performed better (ΔR2>0.2 and ΔRMSE>0.1 g cm-3) for soil samples with low SOC stock (<3 kg m-2). Therefore, we suggest that the local-RFFRFS is a promising method for BD prediction while traditional PTFs would be more efficient when BD is subsequently utilized for calculating SOC stock. Finally, we produced two BD and SOC stocks datasets (18,945 and 15,389 soil samples) for LUCAS Soil 2018 using the best traditional PTF and local-RFFRFS, respectively. This dataset is archived from the Zenodo platform at https://zenodo.org/records/10211884 (Chen et al., 2023). The outcomes of this study present a meaningful advancement in enhancing the predictive accuracy of BD, and the resultant BD and SOC stock datasets across the Europe enable more precise soil hydrological and biological modelling.