bluuebunny/crossref_metadata_2025 huggingface dataset:
Temperature imaging of density currents using phosphor micrometric particles
Authors: Rousseau Gauthier , Pellerin Nathan , Fond Benoît , Blanckaert Koen
Published: March 2024

Recent advancements in fluid flow imaging techniques have made it possible to visualize local temperature in flows by observing the response of photoluminescent dye or particles to light excitation. This has sparked increased interest in exploring laboratory-scale density currents induced by temperature differences. However, unlike the commonly investigated saltwater-freshwater or turbidity currents, heat transfer through boundaries can occur, potentially influencing the dynamics of the density current.In this study, we utilize the dependence of the luminescence persistence time following pulse excitation on ambient fluid temperature of micrometric phosphor particles (YAG:Cr) to spatially and temporally resolve gravity currents produced by a lock-exchange flow. Notably, we introduce a novel inexpensive approach, which combine the use of LEDs and inexpensive high resolution CMOS sensors operated in a multi gate accumulation mode to extract temperature information with high spatial resolution. This simple method holds promise as it significantly enhances the accessibility of high resolution temperature imaging techniques for experimentalists. It can be applied to various thermal fluid experiments, to study for example thermal convection in fluid bodies.
Q-Arctic: A synergetic approach to observe and model pan-Arctic interactions between hydrology and carbon
Authors: Göckede Mathias , Brovkin Victor , Bartsch Annett , Heimann Martin ,   Q-Arctic Team
Published: March 2024

Arctic permafrost has been identified as a critical element in the global climate system, since it stores a vast amount of carbon that is at high risk of being released under climate change. The feedbacks between permafrost carbon and climate change are moderated by complex interactions between physical, hydrological, biogeochemical, and ecological processes. Many of these are not well understood to date, and therefore also only rudimentarily represented in current Earth System Models (ESMs). A particular problem in this context is a scaling gap between processes and model grid.The Q-ARCTIC project funded by the European Research Council (ERC) follows a synergetic approach by combining remote sensing and local-scale observations with modeling on scales from a few meters to hundreds of kilometers. The primary objective of Q-ARCTIC is to close the gap between process scales and the much coarser grid resolution of Earth System Models (ESMs), with a particular focus on the net effect of disturbance processes and associated changes in hydrology on the pan-Arctic scale. To close this gap, we developed new ESM modules representing subgrid through stochastic parameterizations, trained and evaluated with high-resolution remote sensing data and site-level observations.We will present novel results based on in-situ observations that characterize prominent Arctic disturbance features, and satellite remote sensing products investigating fine scale (few meters) patterns in Arctic landscapes that are undergoing modifications linked to climate change. Targets investigated include for example sinking surfaces, wetness gradients in heterogeneous landscapes, or drained lake basins. Assimilation of these new datasets supported the development of new ESM model components that successfully capture the statistics of small-scale features, e.g. depressions linked to sinking surfaces, or surface water bodies that form when soil ice melts. Our results demonstrate that the ability to project the response of the high-latitude water, energy and carbon cycles to rising global temperatures may strongly depend on the ability to adequately represent the soil hydrology in permafrost affected regions.
Assessing measurement noises from low-cost GNSS receivers and antennas
Authors: Anwar Ibaad , Devaraju Balaji
Published: March 2024

Observations from the Global Navigation Satellite System (GNSS) play a crucial role in numerous applications, but are prone to measurement noise, especially when utilizing low-cost receivers and antennas. These measurement noises are crucial as they significantly impact the accuracy and reliability of positional data. This study investigates the characteristics and implications of measurement noises in low-cost GNSS systems, with a particular focus on the effects of receiver and antenna quality, environmental factors, and satellite dynamics. It employs a geometry-free approach to GNSS measurement analysis, aiming to identify and quantify the various noise sources in code-pseudorange and carrier phase observations. The analysis utilized data from two low-cost GNSS stations, each equipped with a u-blox dual-frequency receiver. These stations are equipped with survey-grade and navigational antennas. Additionally, data from the IGS station IITK has been used for comparative analysis.
Surface-consistent Probabilistic Seismic Hazard, Microzonation and Damage Potential Modelling for the City of Nashik, Maharashtra, India
Authors: Biswas Arpita , Nath Sankar Kumar
Published: March 2024

The Koyna-Warna seismic region located in western Indian state of Maharashtra, encompassing the south-western part of the Deccan Volcanic Province, reveals prolific seismicity attributed to both intraplate tectonism and reservoir-triggered activities. The 1967 Koyna earthquake of MW 6.3 marked the largest reported instance of Reservoir-Induced Seismicity, resulting in the destruction of the town of Koynanagar; subsequently, the 1993 Killari earthquake of MW 6.2 claimed thousands of lives with enormous structural damages in Latur district with maximum intensities of IX and VIII respectively. This, therefore, underscores the imperative need for seismic hazard assessment to enhance earthquake disaster preparedness and risk mitigation. Both areal and tectonic sources in two hypocentral depth ranges of 0-25 km and 25-70 km along with 15 Ground Motion Prediction Equations including 6 Site-specific Next Generation Spectral Attenuation models pertaining to Koyna-Warna, Kutch and Central India seismogenic sources have been incorporated to deliver Probabilistic Peak Ground Acceleration (PGA) of Koyna-Warna region at firm rock condition varying from 0.05-0.48g for 10% probability of exceedance in 50 years. Extensive Geotechnical and Geophysical investigations combined with Topographic Gradient data in high-altitude areas have provided the effective shear wave velocity distribution, classifying the region into ten site classes viz. E/F (≤180m/s), D4 (180-240m/s), D3 (240-280m/s), D2 (280-320m/s), D1 (320-360m/s), C4 (360-440m/s), C3 (440-520m/s), C2 (520-620m/s), C1 (620-760m/s) and B (760-1500m/s), leading to a detailed seismic hazard modelling of the ancient holy city of Nashik, the fourth largest city in the state of Maharashtra. 2D nonlinear site response analysis using PLAXIS 2D has been performed for the city, which amplified the bedrock PGA by a factor ranging from 1.75 to 3.18 times, thus generating surface-consistent hazard in the range of 0.14-0.25g. The estimated PGA has further been used for the holistic microzonation integrating multiple geo-hazard themes viz. Surface-consistent Probabilistic PGA, Liquefaction Potential Index (LPI), Site Class, Geomorphology and Geology which categorized the city into five zones based on Seismic Hazard Index (SHI) namely ‘low (0.00
How good are simulations of historical severe storm tides forced by atmospheric reanalysis products in the German Bight?
Authors: Meyer Elke M.I. , Gaslikova Lidia
Published: March 2024

Severe storm tides are one of the main hazards for the German coast. Understanding the development of storm tides and the resulting water levels supports decision-making. We have used a hydrodynamic model to simulate several of the highest observed storm tide events for the locations Norderney, Cuxhaven and Husum (German Bight). The hydrodynamic model is forced by atmospheric (century) reanalysis data (20CR-ensembles, ERA5 and UERRA-HARMONIE) and FES-tides.  In general, the simulations of the severe storms with tracks over Scandinavia and a strong wind gradient over the North Sea show better peak water level results and lower variability compared with more southerly storms with storm tracks over the North Sea. However, the highest observed water level in the German Bight could not be simulated with any of the considered atmospheric forcings. The individual weather situations with the corresponding storm tracks are analysed in order to better understand their different effects on the peak storm tides, their variability and their predictability.
Revealing the key factors and uncertainties in data-driven hydrological prediction using Explainable Artificial Intelligence techniques
Authors: Tian Ye , Tan Weili , Yuan Xing
Published: March 2024

Deep learning models for streamflow prediction have been widely used but are often considered as "black boxes" due to their lack of interpretability. To address this issue, the field has recently focused on Explainable Artificial Intelligence (XAI) methods to improve the transparency of these models. In this study, we aimed to investigate the influence of precipitation uncertainty on data-driven modeling and elucidate the hydrological significance of deep learning streamflow modeling in both temporal and spatial dimensions by Explainable Artificial Intelligence techniques. To achieve this, an LSTM model for time series prediction and a CNN-LSTM model for fusion spatial-temporal information are proposed. These models are driven by five sets of reanalyzed datasets. The contribution of precipitation before peak flow to runoff simulation is quantified, in order to identify the most important processes in runoff generation for each river basin. In addition, visualization techniques are employed to analyze the relationship between the weights of the convolutional layers in our models and the distribution of precipitation features. By doing so, we aimed to gain insights into the underlying mechanisms of the models' predictions.The results of our study revealed several key findings. In the high-altitude areas of the Yangtze River's upper reaches, we found that snowmelt runoff, historical precipitation, and recent precipitation were the combined causes for floods. In the middle reach of the Yangtze River, floods were induced by the combined effect of historical and recent precipitation, except for the Ganjiang River, where historical precipitation events played a major role in controlling flood events. Through the visualization of convolutional layers, we discovered that areas with high convolutional layer weights had a greater impact on the model's predictions. We also observed a high similarity between the weight distribution of the convolutional layers and the spatial distribution of multi-year average precipitation in the upper reach river basins. In the middle reach, the weight distribution of the model's convolutional layers showed a strong correlation with the monthly maximum precipitation in the basin. Overall, this study provides valuable insights into the potential of deep learning models for streamflow prediction and enhances our understanding of the impacts of precipitation in the Yangtze River Basin.
Differences in the cloud, precipitation, and convection representation between the global sub-km mesh simulation and km simulations
Authors: Matsugishi Shuhei , Ohno Tomoki , Satoh Masaki
Published: March 2024

Global nonhydrostatic models that cover the globe with a kilometer (km)-scale mesh have been developed by various organizations worldwide and are expected to be next-generation models that can explicitly calculate deep convective clouds. However, it is known that convective upward motions are not sufficiently represented at the km-scale resolution, and the mesh size of O(100m) is required to obtain convergence of upward motions. To understand the limitation of global km-scale models, we investigate the representation of cloud, precipitation, and circulation with the resolution in the global simulations between km-scale to sub-km-scales.We conduct the global atmospheric simulations by the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for the mesh size of 3.5 km, 1.7 km, 870 m, 440 m, and 220 m using the Supercomputer "Fugaku."  The 3.5 km experiment started on August 1, 2016, the same day as DYAMOND-summer, and the next higher resolution was run using the lower resolution simulation results as initial conditions. We analyzed data on August 5, 2016. We conducted the global 220m simulation for 8 hours.The resolution dependence of cloud, precipitation, and convection was investigated. Lower clouds decrease with increasing resolution. High cloud increased or decreased with respect to resolution depending on the turbulence scheme. The precipitation distribution and zonal mean humidity do not change significantly, but the precipitation intensity changes with resolution. For the grid spacing of less than km, it eliminates overconcentration of precipitation, and the rain area widens as the resolution becomes finer. The coarse-grained rainfall distribution is smoother in the sub-km scale model than in the km scale model. A finer scale convection core is reproduced in the sub-km scale model. Vertical wind speed at grid point scales increases with increasing resolution. However, when horizontally averaged over a few-degree grid, the vertical wind speed decreases, and the circulation becomes weaker with higher resolution. We found that the km-scale model may be creating large strong convection. Uncertainties resulting from the turbulence scheme also appear to be large in the km/sub-km models.
Changes in soil organic carbon content affect plant available water more strongly in subsoil than in topsoil
Authors: Skadell Laura , Dettmann Ullrich , Don Axel
Published: March 2024

With the increase of drought events due to climate change, agricultural production is under pressure to maintain yields. The subsoil (> 30 cm) often harbours unexploited water and nutrient resources that can meet the needs of plants when the topsoil has already dried out. The fertility of the soil is also closely linked to its organic carbon (OC) content. A higher soil organic carbon (SOC) content can improve the soil structure, which is associated with a higher water infiltration rate and a higher water retention capacity and thus can facilitate the adaptation of agriculture to a changing climate. However, the knowledge about quantity changes, especially in subsoils, is insufficient. Therefore, we analysed soils from eleven field sites to quantify the effects of SOC content on topsoil and subsoil plant available water (PAW), here defined as the water content between pF = 1.8 and pF = 4.2. Long-term experiments were set up at four sites, which we sampled after a duration of 57-68 years. In addition, four short- to medium-term trials with a duration of
Mercury deposition and bioaccumulation influenced by sea ice variability in coastal Antarctic ecosystems
Authors: Xue Yulu , Sun Guangyi , Liu Yi , Sun Ruoyu , Liu Xiaodong
Published: March 2024

 Cryptogams, as pioneer plants, are the dominant vegetation in regions with harsh climatic conditions (e.g., polar regions), and play a critical role in the mercury (Hg) biogeochemical cycling. Unlike the Arctic, the ice sheet covers the majority of the Antarctic continent, with vegetation developing only in the ice-free areas along the coast. Although the polar regions are considered globally sinks for Hg, the transport and environmental fate in Antarctic terrestrial ecosystems remain unclear. Here we analyzed the Hg concentration and Hg isotope composition in mosses and lichens from maritime and continental Antarctica, and reviewed previous studies, to investigate the bioaccumulation of Hg by cryptogams and the biogeochemical cycling of Hg. Hotspots of Hg accumulation in cryptogams were found in two different climatic regions (Terra Nova Bay polynya area and James Ross Island). Significant negative Δ199Hg (up to -1.94‰) suggested that atmospheric mercury depletion events (AMDEs) have a prominent contribution to the bioaccumulation of Hg. This process is controlled by sea ice variability, emphasizing the dual roles in the Hg cycle in coastal Antarctic ecosystems. On the one hand, muti-year sea ice cover inhibits sea air exchange and prevents mercury evasion, on the other hand, bromine release from first-year sea ice enhances AMDEs. Antarctic sea ice is undergoing significant changes under global warming, which may modify Hg deposition and bioaccumulation in coastal Antarctic ecosystems.
Human activity has decoupled surface water storage from precipitation in global drylands
Authors: Zhao Gang , Gao Huilin , Li Yao , Tang Qiuhong , Woolway Iestyn , Merder Julian , Rosa Lorenzo , Michalak Anna
Published: March 2024

The availability of surface water in global drylands is essential for local populations and ecosystems. However, the long-term changes in surface water storage and their underlying causes, particularly from anthropogenic activities, remain largely unknown. Here we utilized optical and altimetric remote sensing data to create monthly time series of storage changes between 1985 and 2020 for 105,400 lakes and reservoirs in global drylands. Our analysis reveals that surface water storage in global drylands has been increasing at a rate of 2.20 km3 per year, primarily due to the addition of new reservoirs. For lakes and older reservoirs constructed before 1983, their long-term storage changes are mainly attributed to anthropogenic activities, including human-induced warming and water management, rather than changes in precipitation. These observation-based findings highlight that anthropogenic activities have decoupled surface water storage from precipitation in global drylands, with significant implications for the sustainability of local society and ecosystems.
Nakkihomma: attitudes towards and distributions of academic household work
Authors: Lauri Katja Anniina , Li Xuefei , Dukat Paulina , Atashi Nahid , Karppinen Laura , Lehtipalo Katrianne , Lintunen Anna , Moisseev Dmitri , Mukkala Janne , Nieminen Tuomo , Rantanen Rosa , Vesala Timo , Ylivinkka Ilona , Vehkamäki Hanna
Published: March 2024

The equality and work well-being group at the Institute for Atmospheric and Earth System Research (INAR) at the University of Helsinki conducted a survey about academic household work (AHW) tasks among the institute’s staff in autumn 2023. The main aim of the survey was to find out how different AHW tasks are divided among the staff members and how the staff members consider these tasks.Before the actual survey, we asked the staff to list tasks they consider AHW (nakkihomma in Finnish; direct translation: Frankfurter task). A few examples of AHW tasks we got: sending calendar invitations for meetings, making coffee for others, helping to organize social events at the institute, emotional service work (being involved in discussion with colleagues or students about their personal affairs or problems). For the survey, we grouped the proposed tasks in three categories (number of tasks in parentheses): research-related tasks (3), society-related tasks (4) and community-related tasks (29). The last category was further divided into four subcategories: tasks related to meetings (7), social events (6) and facilities (9), and miscellaneous (7). We asked which tasks the staff members consider as AHW, and how frequently they are committed to each task.We received a total of 91 answers to the survey. This corresponds to 33% of our staff, but according to the background information we collected, the different groups in terms of gender, career stage, language status (Finnish/non-Finnish speaker) and staff group (research/technical/administrative) were represented well.The general attitude towards AHW was surprisingly positive: 57% of respondents had a positive attitude while 35% had a neutral attitude. Senior research staff members use a considerable amount of time participating in different committee meetings while early-career researchers do not so much; however, they do a great deal of practical duties related to meetings. Furthermore, we found out that a lot of emotional service work is being done. Interestingly, early career researchers do not consider this generally as AHW while senior researchers do. Male staff members contribute more to technical writing and guiding tasks while female staff use more of their time in emotional service work and general collective AHW tasks. Finnish speakers contribute more to writing and guiding tasks while non-Finnish speakers are more frequently committed in “catering” AHW like making coffee. Technical and administrative personnel generally contribute more to AHW than research staff.We hope that the results of this survey will help us developing a more equitable and inclusive atmosphere in our institute by enabling us to pay more attention in distributing AHW tasks in a more equal and just manner.
Machine learning based fire danger assessment framework for Indian forests 
Authors: Barik Anasuya , Baidya Roy Somnath
Published: March 2024

We developed a comprehensive fire risk assessment framework for Indian forests, divided into five distinct forest zones (Himalayan, Northeast, Central India, Deccan, and Western Ghats) characterized by diverse climatic conditions and forest types. This framework focused on three primary triggering factors: weather, fuel availability, and anthropogenic ignition.For the weather factor, we considered the Fire Weather Index (FWI) module of the Canadian Forest Fire Danger Rating System with ECMWF's ERA5 reanalysis as meteorological inputs over the period 2003-2021. As fire weather is a dominant factor in causing fires, we developed a robust system to predict fire weather danger. We evaluated the simulated FWI against MODIS active fire data and observed that FWI was a good enough metric for fire weather danger assessment. FWI was categorized into five danger classes through an ensemble approach based on logistic regression, FWI percentiles, percentage of fires, and K-means clustering. We introduced machine learning techniques to reduce the subjective decisions in these methods. This increased the efficiency of the danger rating system to detect fire probability well by 30-50%. A rigorous evaluation of the danger classes revealed that there was no overlap of central tendencies between different methods in the ensemble. The defined danger classes demonstrated coherent values for evaluative parameters, with a consistently high hit rate, low hits due to chance, moderate correct rejections, and an acceptable false alarm ratio.Addressing fuel availability, we used vegetation indices (MODIS normalized difference and enhanced vegetation indices) and topographic features (aspect, elevation and slope from FLDAS land surface model). The anthropogenic ignition factor consisted of population density and land use information. In India, fragmented forests cohabitate with human settlements and agricultural lands. To quantify the impact of anthropogenic ignition on fire occurrences, we computed the percentage of built-up and agricultural area within each grid cell. We used machine learning predictive algorithms such as multiple linear regression with interactions, support vector machines, decision trees and neural networks to integrate these triggering factors with fire count as the target variable, We selected the highest-performing system as the risk assessment framework.This country-scale fire risk assessment provides insights into regional exposure variations and serves as a foundational step towards establishing an operational fire risk assessment system for India. This framework will be of help to operational fire management agencies, enabling enhanced prediction of fire danger and informed decision-making.
Raman spectroscopy analysis of fluid inclusions as a tool in determination of origin of Colombian emeralds.
Authors: Garcia-Toloza Javier , Betancur Camilo , Alvarado Holman , Cedeño Carlos Julio
Published: March 2024

Gemological reports have gained a crucial role in today’s gemstone trading because they provide buyers and sellers with information regarding gem features, which in turn can be translated to monetary value. Although there is information about technics to separate Colombian emeralds from other countries, not much information is available about how to differentiate emeralds from the Western Belt (COC), and those from the Eastern Belt (COR), two main produced zones in Colombia, those districts more recognized are, Muzo and Chivor, respectively. This work aims to show the advances made in one of the techniques used in a gemological laboratory to determine the origin of emeralds.The analysis of fluid inclusions via Raman spectroscopy can yield beneficial information regarding the chemical properties of mineral systems. Primary fluid inclusions trapped by emerald permit us to know the chemistry and the thermobarometric conditions of the mineralizing fluids. Fluid inclusions are usually three-phase inclusions with contain three or more phases including liquids, gases, and salt (halite). Sometimes they are multiphase with solids such as calcite. The gas phases are typically CO2, N2, and CH4 (Cheilletz et al., 1994; Giuliani et al., 1993; Giuliani et al., 1995; Romero & Hernandez, 1999; García-Toloza et al., 2017). Raman spectra of CO2 exhibit two bands near 1285 cm-1 and 1385 cm-1, this feature is well-known as the Fermi doublet (Fernandez, 1983; Howard-Locke, 1971); the distance between these bands is proportional to the fluid density (Rosso & Bodnar, 1995; Song Y. et al., 2003). The data of ∆ Fermi doublet suggest that values below 103.1 are exclusive from the COR, whereas there is an overlap from 103.1 to 103.4. Additionally, the position of the two main CO2 bands varies significantly between the two Colombian belts. The location of peaks from the COR are found at lower energy levels. Thus, this method may be used to estimate the provenance of some populations, the Fermi doublet is not only helpful to differentiate between the Colombian emerald belts but also between Colombian emeralds from samples from Brazil and Afghanistan.
Monthly flood frequency regionalization for comprehensive flood damage assessment to crops
Authors: Scorzini Anna Rita , Paz Idarraga Charlie Dayane , Molinari Daniela
Published: March 2024

Quantitative flood risk assessments rely on damage models, which relate information on flood hazard and vulnerability of exposed assets to estimate expected losses. Differently from other sectors, crop damage depends not only on typical hazards variables (including water depth, flow velocity, inundation duration, water salinity, yield of sediments and/or contaminants) but also on the month of flood occurrence. Indeed, plant vulnerability changes over the different phenological phases that are strictly related to the seasonality of crop production. Considering the time of occurrence of the flood would imply a shift from the traditional representation of inundation scenarios based on annual probability to monthly-based hazard estimations. When risk assessment is carried out at large spatial scale, a detailed understanding of seasonal flood patterns is then required for the different sub-catchments of the basins, including un-gauged ones. In this study we present a clustering approach to flood frequency regionalization applied to the Po River District in Northern Italy, within the risk assessment process required by the European Floods Directive. The  area is characterized by complex climatic and topographic conditions, highlighting the representativeness of the case study for the implementation of the proposed approach in other geographical contexts. Utilizing observed monthly flow data from over 100 gauging stations, the approach combines both physical and statistical criteria to identify homogeneous regions in terms of flood generation mechanisms and seasonality. The process enables the assignment of distinct monthly flood probabilities to all catchments within the district, thereby supporting a comprehensive flood risk assessment for the agricultural sector.
Northern Hemisphere extratropical cyclone biases in ECMWF sub-seasonal forecasts
Authors: Sprenger Michael , Büeler Dominik , Wernli Heini
Published: March 2024

Extratropical cyclones influence midlatitude surface weather directly via precipitation and wind and indirectly via upscale feedbacks on the large-scale flow. Biases in cyclone frequency and characteristics in medium-range to sub-seasonal numerical weather prediction might therefore hinder exploiting the potential predictability on these timescales. We thus, for the first time, identify and track extratropical cyclones in 21 years (2000 – 2020) of sub-seasonal ensemble reforecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) in the Northern Hemisphere in all seasons. Overall, the reforecasts reproduce the climatology of cyclone frequency and life-cycle characteristics qualitatively well up to six weeks ahead. However, there are significant regional biases in cyclone frequency, which can result from a complex combination of biases in cyclone genesis (locally and upstream), size, location, lifetime, and propagation speed. Their magnitude is largest in summer, with the strongest deficit of cyclones of up to 15% in the North Atlantic, relatively large in spring, and smallest in winter and autumn. Moreover, the reforecast cyclones are too deep in both ocean basins during most seasons, although intensification rates are captured well. An overestimation of cyclone lifetime and differences between the native spatial resolutions of the reforecasts and the verification dataset might explain this intensity bias in some cases, but there are likely further so far unidentified processes involved. While the patterns of cyclone frequency and life cycle biases often appear in lead time weeks 1 and 2, their magnitudes typically grow further at sub-seasonal lead times and, in some cases, saturate in weeks 5 and 6 only. Most of the dynamical sources of these biases thus likely appear in the early medium range, but biases on longer timescales probably contribute to their further increase with lead time. Our study provides a useful basis to identify, better understand, and ultimately reduce biases in the large-scale flow and in surface weather in sub-seasonal weather forecasts. Given the considerable biases during summer, when sub-seasonal predictions of precipitation and surface temperature will become increasingly important, this season deserves particular attention for future research.
In Situ Measurements of Sample Thickness in Diamond Anvil Cells Suggest Large Systematic Errors in High-Pressure Conductivities
Authors: Breton Helene , Lobanov Sergey
Published: March 2024

The thermal and electrical conductivities of materials in planetary mantles and cores are crucial for understanding planetary evolution and dynamics. The experimental determination of conductivities at mantle and core pressure-temperature conditions in the diamond anvil cell requires that the sample thickness is precisely known. The standard approach has been to estimate sample thickness from the equations of state of the sample, assuming isotropic contraction (upon compression) or expansion (upon decompression). Recently, however, it has been shown [1] that common pressure media used in diamond anvil cell experiments thin in a strongly non-isotropic manner. If samples embedded in pressure media also deform non-isotropically, then the extant experimental estimates of mantle and core conductivity may contain systematic errors of approximately 30-50% [1]. In situ measurements of sample thickness are needed to verify this inference. We will report on the first in situ interferometric measurements of Fe foil thickness in a diamond anvil cell with sample configurations resembling that of previous experiments to measure the conductivity of Fe. Our preliminary data show that the contraction and expansion of Fe is strongly non-isotropic, potentially explaining the discrepancies in the reported iron conductivity at core-mantle boundary conditions [2, 3]. We will also discuss practical aspects of future measurements of thermal and electrical conductivity of mantle and core materials in a diamond anvil cell. [1] Lobanov, S. S., & Geballe, Z. M. (2022). Non-isotropic contraction and expansion of samples in diamond anvil cells: Implications for thermal conductivity at the core-mantle boundary. Geophysical Research Letters, 49, e2022GL100379. [2] Zhang Y., Hou M., Liu G., Zhang C., Prakapenka V.B., Greenberg E., Fei Y., Cohen R. E., and Lin J.-F (2020) Reconciliation of Experiments and Theory on Transport Properties of Iron and the Geodynamo. Phys. Rev. Lett. 125, 078501. [3] Ohta, K., Kuwayama, Y., Hirose, K. et al. (2016). Experimental determination of the electrical resistivity of iron at Earth’s core conditions. Nature 534, 95–98. 
Evaluating the seasonal patterns of low flow in  Nakdong River basin using SWAT
Authors: Kim Wonjin , Choi Si Jung , Kang Seong Kyu
Published: March 2024

ABSTRACTThis study focuses on the seasonal patterns of low flow in the Nakdong River basin (23,635 km2), considering its vital role as a seasonal phenomenon and integral component of the flow regime. Low flow, derived from groundwater discharge or surface discharge from lakes and reservoirs, exhibits varying magnitudes and durations under seasonal changes, thereby holding significant implications for agricultural activities, aquatic species, and water quality. In the absence of gauge stations for small streams, Soil and Water Assessment Tool (SWAT) was employed to ensure reliable simulation for low flow along the target watershed, and the model was calibrated for the period of ten years (2010~2019) using observed data from multipurpose dams and multifunctional weirs within the target watershed. Based on the model results, spatio-temporal variations of low flow were estimated, and seasonality indices were adopted by means of understanding and analysing low flow characteristics. The indices include seasonlity histograms (SHs) depicting the monthly distribution of low flows, seasonlity index (SI) representing the average timing of low flows within a year, and seasonality ratio (SR) showing the ratio of summer to winter low flows. Subsequently, seasonal patterns of low flow in target watershed were evaluated under three indices to figure out the response of low flow in relation to watershed characteristics and climate variability. AcknowledgementsResearch for this paper was carried out under the KICT Research Program, Development of IWRM-Korea Technical Convergence Platform Based on Digital New Deal) funded by the Ministry of Science and ICT.
How Has the Ferrel Cell Contributed to the Maintenance of Antarctic Sea Ice at Low Levels Since 2016?
Authors: Wang Shaoyin , Liu Jiping , Wei Zixin , Li Hua , Yang Dongxia , Cheng Xiao
Published: March 2024

This study investigates the specific circulation anomalies that have sustained the low Antarctic sea ice state since 2016. Firstly, we find a significant strengthening and southward shift in the Ferrel Cell during 2016–2022, resulting in a marked increase in zonally southward transport of heat and moisture. Secondly, this enhanced Ferrel Cell is closely associated with a stronger circumpolar wave pattern (CWP) over the same period. This pattern is zonally asymmetric and greatly amplifies the poleward advections of heat and moisture, leading to the increased downward longwave radiation, more liquid precipitation and sea ice retreat in specific regions, including the western Pacific and Indian Ocean sectors, eastern Ross and northern Weddell Seas. The strong correlation between the Ferrel Cell and CWP was reproduced by the Community Earth System Model - Large Ensemble. As global warming continues, the potential southward shift of the Ferrel Cell poses a major threat to sea ice retreat.
Quantifying drought legacy effects in a Mediterranean oak forest using eddy covariance, sap flow, and stem growth data
Authors: Heinrich Sarah , Yu Xin , Bastos Ana , Hoek van Dijke Anne , Limousin Jean-Marc , Werner Christiane , Orth René
Published: March 2024

The frequency and intensity of droughts is expected to increase with climate change. Droughts can affect vegetation through e.g. hydraulic failure, depletion of carbon reserves and reduced growth, which in turn can influence ecosystem functioning beyond the duration of the drought. Previous studies have provided examples of drought legacy effects for example by reduced gross primary productivity (e.g. in Yu et al. 2022). It remains unclear whether legacy effects occur in drought-resistant mediterranean oak forests and to what extent those effects can be detected across water- and carbon-related vegetation variables.This study investigates legacy effects on gross primary productivity (GPP), sap flow and stem growth of holm oak (Quercus ilex L.) using measurements from an evergreen Mediterranean forest in southern France during the time period 2000 to 2015. We jointly analyze sap flow and growth measurements from several trees located within the footprint of the eddy covariance GPP measurements. In order to isolate the legacy effects, we follow the approach of Yu et al. (2022) using a random forest regression model to predict potential sap flow or gross primary productivity based on concurrent hydro-meteorological conditions and compare potential and actual values. The same approach was applied to stem growth data, but using a linear regression model due to the fewer observations available.Our study shows that a drought in 2006 caused comparable drought legacy signals across all considered vegetation variables. A slight positive effect, i.e. higher measured values than predicted, was diagnosed in the first post-drought year and no legacy effects were detected in the second post-drought year. The results suggest that Q. ilex shows a fast and complete recovery after the first post-drought year, which can be expected as it is a drought-adapted species. We also observe a large variability in legacy effects across individual trees, which suggests that individual tree properties and local soil characteristics might affect their drought sensitivity and resilience. Jointly analyzing drought legacy effects across different scales and variables opens the possibility of a holistic understanding and thus helps to improve the representation of drought effects in predictions of the future land carbon sink. Reference: Yu, X., Orth, R., Reichstein, M., Bahn, M., Klosterhalfen, A., Knohl, A., Koebsch, F., Migliavacca, M., Mund, M., Nelson, J. A., Stocker, B. D., Walther, S., & Bastos, A. (2022). Contrasting drought legacy effects on gross primary productivity in a mixed versus pure beech forest. Biogeosciences, 19(17), 4315–4329. https://doi.org/10.5194/bg-19-4315-2022
The Role of the Meridional Rossby wave for Extreme Heatwaves Over East Asia
Authors: Noh El , Kim Joowan
Published: March 2024

The North Pacific High is a dominant circulation system that governs the weather in the East Asian region during the summer, and its western boundary serves as a waveguide for the propagation of Rossby waves from the equatorial to mid-latitudes. The deep convection over the equatorial western Pacific usually creates Rossby waves that propagate northward along this waveguide. This meridional Rossby wave, known as the Pacific-Japan (PJ) pattern, is the dominant teleconnection pattern in the vicinity of East Asia, and it often accompanies Heatwaves.In this study, the circulation and thermodynamic characteristics of the PJ pattern were investigated based on a daily timescale to better understand their relationship with the likelihood of heatwaves in East Asia. According to thermodynamic budget calculations, horizontal heat advection crossing the climatological flow pattern is the key factor for the observed surface air warming. The circulation pattern associated with a PJ pattern largely explains the enhanced warm advection. The overall findings of this study provide valuable insights into the development mechanisms of heatwaves on an intraseasonal timescale.
Improved Least Travel Time ray-tracing operator for GNSS tropospheric delays
Authors: Vasiuta Maksym , Navarro Trastoy Angel , Tuppi Lauri , Motlaghzadeh Sanam , Järvinen Heikki
Published: March 2024

Modelling of the microwave signal delay in the neutral atmosphere (i.e., the tropospheric delay) is a crucial part of GNSS observations processing. The design of observation-modelling algorithms is based on signal ray tracing. Considering advancements in modern Numerical Weather Prediction (NWP) models and high standards of GNSS product quality, it is necessary to revise the existing ray-tracing algorithms. We developed an improved least-travel time (LTT) ray-tracer with robust physics assumptions, based on the original LTT algorithm. Both new and original LTT implementations, along with the state-of-the-art VieVS Ray-tracer (RADIATE), are supplied with numerical weather data by the Open Integrated Forecasting System model (OpenIFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). These three ray tracers are justly compared in the setup of modelling the skyview delays for 256 GNSS stations during one month (December 2016). The skill of these delay products is assessed as the quality of GNSS precise orbit determination (POD) products of the GPS constellation made by the orbit solver GROOPS (Gravity Recovery Object Oriented Programming System) software toolkit of the Graz University of Technology. The GNSS POD metrics which have been analysed are orbit midnight discontinuities (MD) and precise point positioning (PPP) error. In the context of these metrics, the usage of the new LTT algorithm leads to better orbit products, compared to the original LTT and the RADIATE ray tracers.
Intensification of daily tropical precipitation extremes from more organized convection
Authors: Bao Jiawei , Stevens Bjorn , Kluft Lukas , Muller Caroline
Published: March 2024

Tropical precipitation extremes and their changes with surface warming are investigated using global storm resolving simulations and high-resolution observations. The simulations demonstrate that the spatial organization of convection at mesoscale, a process that cannot be physically represented by conventional global climate models, is important for the variations of tropical daily precipitation extremes (total accumulations over a day). In both the simulations and observations, daily precipitation extremes increase in a more organized state, in association with larger, but less frequent, storms. Repeating the simulations for a warmer climate results in a robust increase in monthly-mean daily precipitation extremes. Higher precipitation percentiles have a greater sensitivity to convective organization, which is predicted to increase with warming. Without changes in organization, the strongest daily precipitation extremes over the tropical oceans increase at a rate close to Clausius-Clapeyron (CC) scaling. Thus, in a future warmer state with increased organization, the strongest daily precipitation extremes over oceans increase at a faster rate than CC scaling. Moreover, as the precipitation distribution becomes more uneven with increased organization, the tropics may not only face heavier precipitation extremes, but experience more extensive drying.
Mergers as the Maintenance Mechanism of Cutoff Lows: A Case Study over Europe in July 202
Authors: Yamamoto Koryu , Iga Keita , Yamazaki Akira
Published: March 2024

A cutoff low that covered Central Europe in the middle of July 2021 brought heavy rainfall and severe flooding, resulting in more than 200 fatalities. This low was formed by a trough on 11 July and merged with another cutoff low around 12–13 July. Analysis of the energy budget and potential vorticity suggests that the main cutoff low was maintained through the merger with another cutoff low; this was the dominant contributor to maintenance of the main cutoff low around 12–13 July. The results of Lagrangian trajectory analyses support this conclusion. Analysis of diabatic PV modification during the merger indicates that radiation acts mainly to enhance the potential vorticity of the parcels when they move from another cutoff low into the main cutoff low, especially in the upper layer (~ 350 K). However, that effect is not pronounced in the lower layer (~ 330 K). These results demonstrate that cutoff lows can be maintained through the merger with another cutoff low and underline the need to consider diabatic processes when investigating mergers.
Exploring the performance of machine learning models for the GNSS-IR retrieval of seasonal snow height
Authors: Aichinger-Rosenberger Matthias , Soja Benedikt
Published: March 2024

Snow is a key variable of the global climate system and the hydrological cycle, as well as one of the most critical sources of freshwater. Therefore, measurements of snow-related parameters such as seasonal snow height (SSH) or snow-water-equivalent (SWE) are of great importance for science, economy and society. Traditionally, these parameters are either measured manually or with automated ground-based sensors, which are accurate, but expensive and suffer from low temporal and spatial resolution.A new alternative for such systems is the use of GNSS observations, by application of the GNSS interferometric reflectometry (GNSS-IR) method. The technique enables users to infer information about soil moisture, snow depth, or vegetation water content. Signal-to-Noise Ratio (SNR) observations collected by GNSS receivers are sensitive to the interference between the direct signal and the reflected signal (often referred to as “multipath”). The interference pattern changes with the elevation angle of the satellite, the signal wavelength, and the height of the GNSS antenna above the reflecting surface. By comparing this reflector heights estimated for snow surfaces with those from bare soil conditions, snow height can be determined.The estimation of reflector heights, and respectively SSH, is typically carried out using Lomb-Scargle Periodogram (LSP) spectrum analysis. This study investigates the potential of machine learning methods for this task, using similar input parameters as the standard GNSS-IR retrieval. Results from different supervised algorithms such as Random Forest (RF) or Gradient Boosting (GB) are shown for different GNSS sites and experimental setups. First investigations indicate that snow heights can be successfully obtained with machine learning, with results less noisy than with classical approaches.