bluuebunny/crossref_metadata_2025 huggingface dataset:
An increasingly turbulent Arctic Ocean?
Authors: Rippeth Tom P.
Published: February 2023

Historically, the Arctic Ocean has been considered an ocean of weak turbulent mixing. However, the decline in seasonal sea ice cover over the past couple of decades has led to increased coupling between the atmosphere and the ocean, with potential enhancement of turbulent mixing. Here, we review studies identifying energy sources and pathways that lead to turbulent mixing in an increasingly ice-free Arctic Ocean. We find the evolution of wind-generated, near-inertial oscillations is highly sensitive to the seasonal sea ice cycle, but that the response varies greatly between the continental shelves and the abyssal ocean. There is growing evidence of the key role of tides and continental shelf waves in driving turbulent mixing over sloping topography. Both dissipate through the development of unsteady lee waves. The importance of the dissipation of unsteady lee waves in driving mixing highlights the need for parameterization of this process in regional ocean models and climate simulations.
A positive definite solution for an EDMF PBL scheme that includes a moist adjustment process
Authors: Bao Jian-Wen , Grell Evelyn , Michelson Sara , Hong Song-You
Published: February 2023

The eddy diffusivity and mass flux (EDMF) scheme for simulating turbulent transport in the operational Global Forecast System (GFS) shows a behavior due to a physical and numerical inconsistency in the scheme's numerical procedure to obtain detrained cloud water due to the moist nonlocal mixing and its associated moist adjustment.  One-dimensional simulations show that such inconsistency leads to an unphysical distribution of thermal tendencies and detrained cloud water near the simulated planetary boundary layer (PBL) top.  To solve this problem, a new procedure to obtain a positive definite solution from the scheme is proposed to solve the EDMF equations in the scheme. We will show the impact of this new solution procedure on the GFS performance.
Model simulation of the aerosol perturbation on the Tibetan Plateau convective precipitation
Authors: Jiang Mengjiao , Li Yaoting , Hu Weiji , Yang Yinshan , Brasseur Guy
Published: February 2023

The Tibetan Plateau (TP) is important for weather and climate. Relatively clean aerosol conditions over the Plateau makes the study on the aerosol-cloud-precipitation interactions in this region distinctive. A convective event with precipitation observed on 24 July 2014 in Naqu was selected to explore the influence of aerosols on the onset and intensity of precipitation. We use the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis to derive the cloud condensation nuclei (CCN), which can be regarded as the real-time background. These values are adopted to initialize the regional WRF 4.0 meteorological model and to simulate the onset of convective events and the formation of precipitation. Four sets of experiments were adopted for our simulations. A detailed analysis of microphysical processes shows that, with the increase in the aerosol number concentration, the conversion rate of cloud water to rain in clouds is enhanced at first. Under polluted situation, the conversion process of cloud water to rain is suppressed; however, the transformation of cloud water to graupel and the development of convective clouds are favored. As a result, the onset of the precipitation is delayed and cold-rain intensity increases.
Landslide Susceptibility within the binomial Generalized Additive Model
Authors: Loche Marco , Alvioli Massimiliano , Marchesini Ivan , Lombardo Luigi
Published: February 2023

We develop a slope-unit based landslide susceptibility model using the benchmark dataset proposed in the session, located in Central Italy. As a result, we produce two susceptibility maps based on the two different landslide presence attribute fields included in the dataset.The proposed dataset is a subset of a much larger one, recently used to obtain landslide susceptibility all over Italy. We further explore the differences between results obtained from the proposed dataset, and landslide susceptibility obtained at national scale. The national scale results were obtained in a Bayesian version of a binomial Generalized Additive Model (GAM) in R-INLA, an R implementation of the integrated nested Laplace approximation for approximate Bayesian inference. The method can explain the spatial distribution of landslides using a family of Bernoulli exponential functions.This allows us to estimate fixed effects and random effects, and to assess their associated uncertainty. The residual susceptibility maps and the most common correlations permit to measure the strength and direction of the relationships between models and to capture differences in susceptibility values across the study area. On their basis, we offer a convenient approach to evaluate the similarities in case of both represented landslide distributions.We propose this modeling comparison for any susceptibility maps to evaluate the interpretability of the covariates and performances, where a large dataset may influence the susceptibility pattern over space.
Soil loss by water erosion assessment uncertainties – experiences from South Africa
Authors: Baade Jussi , Zoller Kevin , Le Roux Jay
Published: February 2023

The Universal Soil Loss Equation (USLE) was initially developed to support the implementation of conservation measures to minimize soil loss by water erosion, i.e. sheet and rill erosion, on a local scale and in the context of agricultural land use. The approach was refined over decades and became the Revised Universal Soil Loss Equation (RUSLE).  At the same time the scope of applications has grown considerably. Due to its rather simple structure and relatively low demand for input data, it has been used for the assessment of soil loss from water erosion for ever growing spatial entities, i.e. regional scale catchments or whole countries. Recently this has been applied on a global scale in order to identify global hotspots of soil erosion. This coherent approach for a global comparison is most welcome against the background of the large number of country-specific assessments which are rather difficult to compare.However, there are two issues of concern. First, one needs to remember that RUSLE-derived soil loss assessments do not account for gully erosion, which might not be linearly scaled with sheet and rill erosion. Furthermore, information on the uncertainties of RUSLE based erosion assessments are not frequently reported. This especially concerns the impact of specific combinations of varying unique input data on the results.In this contribution we compare in high spatial resolution the results of two RUSLE-based soil loss by water erosion assessments conducted for six 100 by 100 km large study sites in South Africa. The first assessment was conducted about two decades ago and was based on the then available data covering the whole of South Africa. The second assessment is a revision, which includes the latest input data for rainfall erosivity, soil erodibility, the topographic factor as well as land cover and management. When compared, the results of the current soil loss estimates are an order of magnitude lower than the previous estimates. Does this difference represent a temporal trend or just the inherent uncertainties reflecting different input data and slightly different data processing? This is the question discussed in this contribution.
Validation of the magnetized ICME model with a multi-spacecradt study in Icarus
Authors: Baratashvili Tinatin , Grison Benjamin , Schmieder Brigitte , Poedts Stefaan
Published: February 2023

Coronal Mass Ejections (CMEs) are the main drivers of interplanetary shocks and space weather disturbances. Strong CMEs directed towards Earth can have a severe impact on our planet and their timely prediction can enable us to mitigate (part of) the damage they cause. One of the key parameters that determine the geo-effectiveness of a CME is its internal magnetic configuration.The novel heliospheric wind and CME propagation model Icarus (Verbeke et al. 2022) which is implemented within the framework of MPI-AMRVAC (Xia et al., 2018) introduces new capabilities for better and faster space weather forecasts. Advanced numerical techniques, such as solution adaptive mesh refinement (AMR) and radial grid stretching are implemented. The different refinement and coarsening conditions and thresholds are controlled by the user. These techniques result in optimized computer memory usage and a significant execution speed-up, which is crucial for forecasting purposes. In this study we validate a new magnetized CME model in Icarus by simulating  interplanetary coronal mass ejections (ICMEs).  We chose particular CME events observed at different radial distances from the Sun by MESSENGER and ACE. We aim to model two CME events, to examine the capabilities of the model in different configurations. We identify the originating active region for the CME of interest, reconstruct its characteristic parameters and initiate the CME propagation inside Icarus with a spheromak CME model. We focus on estimating the accuracy of the arrival time, the shock strength and the magnetic field components of the CME model in Icarus. Using observations of different satellites we can track the propagation of the CMEs in the heliospheric domain and assess the accuracy of the model at different locations.Different AMR criteria are used to achieve higher spatial resolutions at propagating shock fronts and in the interiors of the ICMEs. This way the complex structure of the magnetic field and the deformation and (plasma and magnetic flux) erosion can be simulated with higher accuracy due to the advantage of AMR. Higher resolution is especially important for the spheromak model, because the internal magnetic field configuration affects the CME evolution and its interaction with the magnetized heliospheric wind significantly. We assess the capabilities of AMR at different locations in Icarus. Finally, the obtained synthetic time-series of plasma quantities at different satellite locations are compared to the available observational data. As a result, Icarus allows us to model CMEs with higher accuracy, yet efficiently.TB acknowledges support from the European Union’s Horizon 2020 research and innovation program under No 870405 (EUHFORIA 2.0) and the ESA project “Heliospheric modeling techniques” (Contact No. 4000133080/20/NL/CRS).
"C4TS IN SPACE" project – Carrying Four Tiny Sensors with a high-altitude balloon at the upper limit of the atmosphere
Authors: Bejan Diana Cristina
Published: February 2023

                   The desire to explore cosmic space, the curiosity that leads to discoveries, and the joy of knowledge are the intense feelings that each of us possesses. Students of any age are keen to learn new things, but in the 21st-century learning has to get new valences, its instruments being stronger and more creative. Motivated by the astronauts and space exploration researchers, but also by their passions, a group of adolescents, and 10th-grade students from the National College of Computer Science, Piatra-Neamt, Romania, made an amazing journey into the knowledge universe. This poster reveals this extraordinary learning project, a real and challenging adventure. The purpose of the “C4TS in Space” project, was to build and successfully launch a high-altitude balloon filled with Helium, which would climb to the upper limit of the atmosphere, where the cosmic space begins, then descend with a parachute and be recovered.                    Over a year and a few months, the students have documented, deepened, and correlated the notions of mechanics and thermodynamics and made models of the ascension and descent of the balloon for different values ​​of its payload weight. Students chose the parameters of the atmosphere to be measured and the sensors that could be used, built the hardware, and programmed the data acquisition device. They also studied and documented how to directly communicate data from sensors to the ground. Given that the balloon has been crossing areas with air traffic, a thorough documenting of air legislation and completing all the necessary formalities for the legality of the flight was needed.                   The whole project was a success, both in terms of launching/recovering the balloon and from an educational point of view. The feelings the students have experienced in processing data and watching videos from the balloon, at the limit of cosmic space, led them to want to participate in more projects and made them aware of the complexity of their acquired knowledge.                    Likewise, for me as an educator, the project has been an opportunity to guide students through open learning approaches.
Meteo-Seismology: Harvesting the Seismic Signals of Weather Dynamics in the Critical Zone
Authors: Dietze Michael , Mohr Christian , Tolorza Violeta A. , Sotomayor Benjamin , Gonzalez Erwin
Published: February 2023

Weather conditions are an important driver of Earth surface dynamics, such as gravitational mass wasting, flood propagation, biological activity events and physical interactions within the critical zone. While there are dedicated sensors to capture meteorological parameters, these sensors are comparably expensive, have a small spatial footprint and often lack the temporal resolution needed to constrain high frequency meteorological dynamics. We introduce the concept of meteo-seismology, i.e. the measurement of first-order ground motion signatures of weather conditions by decisively installed seismic sensors. While meteorological manifestations are generally considered seismic noise and it may seem odd to use seismometers instead of weather stations, geophysical sensors circumvent or complement the above caveats and add further important data to a comprehensive picture of the rapidly changing state of the atmosphere and its interaction with the landscape we live in. Based on examples from prototype forested landscapes in Central Europe and Chilean Patagonia, we demonstrate how seismic stations can be used to infer properties of the pressure and wind field and its coupling to the biosphere, constrain rain intensity and drop properties, yield temperature proxies and their propagation into the ground, and survey ground moisture trends and discharge patterns. Understanding the seismic signatures of a meteorological origin also allows to, vice versa, better handle the contaminating side of these seismic sources in records, where high frequency signals are to be used for other than meteo-seismological studies. Our approach offers an alternative and complementary way to non-invasively monitor hydrometeorological energy and matter fluxes at high temporal and spatial resolution.
A Decision Support Tool for soil erosion in agricultural fields in the European Union and China
Authors: Falcao Raquel , Krasa Josef , Dostal Tomas , Gomez Jose , Llanos Maria , Guzmán Gema
Published: February 2023

Within the scope of the TUdi project, EU Horizon 2020 Grant Agreement No 101000224, a questionnaire was responded to by more than 400 farmers from seven countries in Europe and China, aiming to assess farmers' major concerns and develop Decision Support Tools targeted at these issues. One tool currently being developed targets soil erosion and seeks to provide farmers with publicly available historical satellite imagery in a user-friendly platform, as well as guidelines on easy-to-use methodology to assess the degree of soil erosion on the field. The questionnaires also revealed that many farmers do not use official data or digital maps to help make management decisions but are willing to apply simple tools for assessing soil degradation processes. With this, the project hopes to contribute to democratizing access to science, helping farmers – ultimately, the ones who implement soil conservation practices – to make data-driven decisions on soil management and exploring alternatives. The preliminary version of this tool will be presented at the workshop.Research has been supported by project TUDI (European Union's Horizon 2020 research and innovation program under grant agreement No 101000224)
Prediction Challenges from Errors in Tropical Pacific Sea Surface Temperature Trends
Authors: L'Heureux Michelle , Tippett Michael , Wang Wanqiu
Published: February 2023

Initialized, monthly mean predictions of the tropical Pacific Ocean are made against the backdrop of a warming climate, and it is unclear to what extent these predictions are impacted by trends.  Here, we analyze the forecast models that comprise the North American Multi-Model Ensemble (NMME) and uncover significant linear trend errors that have consequences for the tropical Pacific basin and ENSO variability.  All models show positive trend errors over the eastern equatorial Pacific over the 1982-2020 hindcast and real-time period.  These positive trend errors interact with the mean bias of each respective model, reducing, over time, the bias of models that are too cold and increasing the bias of models that are too warm.  These trend errors lead to a tropical Pacific that is too warm and too wet over the basin, and is significantly correlated with an increase in El Niño false alarms.  Finally, we explore the consequences of these tropical Pacific Ocean trend errors on predictions of global precipitation anomalies. 
Estimating the origin of precipitation: uncertainties associated with atmospheric moisture tracking models
Authors: Keune Jessica , Benedict Imme , Weijenborg Chris , Schumacher Dominik L. , Koppa Akash , Miralles Diego G.
Published: February 2023

Precipitation is the largest freshwater flux that enables life on land. However, climate change is projected to increase the frequency and intensity of extreme precipitation events, which often culminate in droughts and floods with devastating impacts on humanity and the environment. To better understand the dynamics of precipitation in a changing climate, recent studies aimed to unravel the interaction between evaporation and precipitation. To do so, atmospheric moisture tracking models have often been employed to determine the origins of precipitation and establish source–sink relationships. However, due to the lack of sufficient and accurate observations to evaluate these models, their estimated source regions of precipitation often remain unvalidated. Nonetheless, the number of studies using such models is increasing, even if only a few have addressed associated uncertainties and even fewer employed multiple models.Here, we advocate the need for moisture tracking model intercomparisons to advance this field of study. Therefore, we provide an overview of models and methods that track moisture through the atmosphere and determine the origins of precipitation. Further, we highlight conceptual differences between these models and demystify assumptions hidden in the analysis of source regions. Using selected case studies, we illustrate the uncertainty associated with the origin of precipitation and highlight the need for coordinated model comparisons using multiple models. Finally, we present our plans to engage with the entire moisture tracking community to collaborate on prospective model intercomparison studies. Through these efforts, we wish to raise awareness about the uncertainties inherent in moisture tracking approaches and achieve a better understanding of the drivers of precipitation in a changing climate.
Multi-method characterization of Asbestiform Antigorite from South Australia
Authors: Menini Alessandro , Truong Bao Han , Fitzgerald Sean
Published: February 2023

Antigorite occurs in several locations worldwide and is defined as the simple serpentine Mg3Si205(OH)4 polymorph that compensates for the silica tetrahedral layer (Si2O5 sheet) misfit with the octahedral brucite Mg(OH)2 layer by regular structural undulation down the “b” crystalline axis. The chrysotile polymorph of the same chemistry compensates for the misfit in full rolls or scrolls along “b” axis, which can form fine fibres, and is the most common mineral in commercial asbestos. Chrysotile is therefore always asbestiform, but antigorite can exhibit a variety of crystalline habits in hand sample and the microscope, from massive, to platy, to bladed, and (occasionally) fibrous, but is not regulated as asbestos.
Here, we performed a multi-method study in order to characterize a highly fibrous asbestiform occurrence of antigorite from Rowland Flat, South Australia. In comparison to HSE Canadian chrysotile, we show that optical (PLM) and transmission electron microscopy (TEM) allows to distinguish this antigorite from chrysotile. Based on PLM analysis with CS dispersion staining, chrysotile is magenta parallel (ǁ) to the polarizer and exhibits blue perpendicular (Ʇ) colours while this antigorite exhibits gold to golden magenta ǁ and blue-magenta Ʇ colours (1.550 HD RI at 25◦C). Also, we demonstrate SAED-TEM patterns for the two specimens to distinguish chrysotile “enrolled” crystal structure from the undulating antigorite structure, focusing on the highly characteristic [110] zone rel-rod streaking versus systematic repeats.
Scanning electron microscopy (SEM) images were collected to further investigate the asbestiform nature of the antigorite from Rowland Flat. We show that the dominant morphology of Rowland Flat antigorite is of microscopic laths that split into very thin needles. In order to further investigate the morphometric parameters of this fibrous antigorite, individual fibres were measured at 20,000-25,000x by TEM. We show that the majority of these structures meet or exceed WHO fibres criteria, display high mean aspect ratios, ranging from 20:1 up to more than 100:1, and display widths lower than 1 µm, which in the literature is definition for asbestos fibres.
Finally, our data indicate that the antigorite from Rowland Flat exhibits all characteristics of the asbestiform habit as defined in international standards (i.e., EPA/600/R-93/116), supporting the need for regulation of asbestiform antigorite as asbestos.
Predicting future groundwater recharge scenario in the Punjab region of India using machine learning techniques
Authors: Banerjee Dolon , Ganguly Sayantan
Published: February 2023

Over last few decades the Punjab region of India has been one of the country's leading contributors to agricultural products. The agricultural farms in the region are supplied with water from a well-established canal system and groundwater reserve in the state. The share of irrigated area in the region fed by canals and groundwater wells are 28 and 72%, respectively. The over and unscientific usage of groundwater over the years has resulted in groundwater depletion at an alarming rate. To help policymakers address the situation and develop effective plans, forecasting groundwater recharge for the future is utmost essential. The recharge process primarily governs the growth or depletion of groundwater reserve. Groundwater recharge is one of the most difficult phenomena to be quantified as it cannot be measured directly and is influenced by several processes varying spatially and temporally. Extensive research work for quantifying the groundwater recharge have been performed in the past. These investigations introduced a number of methodologies, including chemical tracers and physical procedures. These methods, however, being experimental in nature, involve significant time and investment. The use of machine learning algorithms to predict the recharge is promoted as a solution to these problems. These algorithms have proven to be efficient enough to deduce the recharge with very high accuracy. Through a variety of models, ranging from the most basic to one of the more intricate, we have attempted to forecast the recharge scenario in the Punjab region, India. Four machine learning algorithms, namely the Multi-linear regression model, Non-linear regression model (Random Forest), Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) have been employed in this study. The aim was to comprehend the dependence of groundwater recharge on the factors of temperature, precipitation, soil type, LULC, and ground slope. The observed recharge for every subsequent month in a 30-year period is calculated using the observed monthly groundwater level data from observation wells located throughout Punjab. The monthly temperature and precipitation data are used for the study while soil type and ground slope for the location of the observation stations are extracted from digital elevation models (DEMs). At intervals of three years, the LULC maps are created. The models are then used to forecast and compare with the available observation data after the entire data set was split into a training and testing set using the 80/20 method. The models were then assessed for their ability to predict observational data using the Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) in each case. The groundwater recharge prediction is then performed using the model with the highest accuracy.
Advancing Stein Variational Gradient Descent for geophysical uncertainty estimation
Authors: Izzatullah Muhammad , Ravasi Matteo , Alkhalifah Tariq
Published: February 2023

Stein Variational Gradient Descent (SVGD) is a powerful algorithm for uncertainty quantification algorithm introduced by the statistics community that has recently found applications in geophysical inverse problems. It is a non-parametric, iterative method for making probabilistic predictions by sampling from a set of particles, which are updated using gradient information of the Kullback-Leibler divergence between the target posterior distribution and a user-defined proposal distribution. One of the main benefits of SVGD is its ability to handle complex, high-dimensional distributions. This is particularly useful in geophysics, where datasets can be large, and the subsurface model of interest is high-dimensional. However, the computational cost of SVGD increases with the number of particles used to characterize the posterior. Its performance also depends on the choice of the prior distribution, as it influences the SVGD ability to provide geophysical and geological realism in the posterior samples.

There have been efforts to improve the efficiency of SVGD for large data sets, such as by introducing mini-batch techniques and deep learning-based prior aimed at producing high-resolution posterior samples. Along this direction, we are advancing the frontier of the SVGD algorithm by coupling it with the Plug-and-Play (PnP) framework, which allows sampling from a regularized target posterior distribution, where the target posterior distribution is regularized by a convolutional neural network (CNN) based denoiser. We showcase its ability to produce high-resolution, geologically trustworthy samples representative of the subsurface structures on a variety of geophysical problems for reservoir characterization and velocity model building (e.g., full waveform inversion).
Spatially resolved imaging spectroscopy with the Spectrometer/Telescope for Imaging X-rays on-board Solar Orbiter
Authors: Volpara Anna , Massa Paolo , Massone Anna Maria , Piana Michele
Published: February 2023

The fundamental science objective behind solar X-ray imaging spectroscopy is to gain information on the electrons accelerated by magnetic reconnection and on the temperature of the correspondingly heated plasma throughout the whole flaring volume. This talk will prove that the visibility-based technology at the base of the Spectrometer/Telescope for Imaging X-rays (STIX) allows the construction of electron flux and differential emission measure maps that are nicely smoothed along the energy and temperature directions, respectively. Using this approach, we will perform a spatially resolved analysis of the electron flux spectra associated with hard X-ray emissions measured by STIX and discuss the spatially resolved consistency of such emissions with a thermal distribution of the electrons in the flaring source.
TCDetect: A new method of Detecting the Presence of Tropical Cyclones using Deep Learning
Authors: Galea Daniel , Kunkel Julian , Lawrence Bryan
Published: February 2023

Tropical cyclones are high-impact weather events which have large human and economic effects, so it is important to be able to understand how their location, frequency and structure might change in a future climate.Here, a lightweight deep learning model is presented which is intended for detecting the presence of tropical cyclones during the execution of numerical simulations for use in an online data reduction method. This will help to avoid saving vast amounts of data for analysis after the simulation is complete. With run-time detection, it might be possible to reduce the need for some of the high-frequency high-resolution output which would otherwise be required.The model was trained on ERA-Interim reanalysis data from 1979 to 2017 and the training concentrated on delivering the highest possible recall rate (successful detection of cyclones) while rejecting enough data to make a difference in outputs.When tested using data from the two subsequent years, the recall or probability of detection rate was 92%. The precision rate or success ratio obtained was that of 36%. For the desired data reduction application, if the desired target included all tropical cyclone events, even those which did not obtain hurricane-strength status, the effective precision was 85%.The recall rate and the Area Under Curve for the Precision/Recall (AUC-PR) compare favourably with other methods of cyclone identification while using the smallest number of parameters for both training and inference. Work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-843612
Review of coordinated measurements with Swarm and Cluster : multi-scale magnetospheric and ground currents
Authors: Dunlop Malcolm , Dong Xiangcheng , Tan Xin , Yang Junying , Wei Dong , Xiong Chao
Published: February 2023

We review a number of studies, undertaken during the 10 years of Swarm, using distributed, multi-scale measurements arising from the combination with Cluster, MMS and ground based arrays. The scaling, coherence and correlation of field-aligned current sheets (FACs) connecting different regions of the magnetosphere have been explored with associated analysis techniques using these multi-point measurements at both low (LEO) and high altitudes, and in relation to (R1/R2) auroral boundaries. Individual events can map to conjugate current density distributions at the magnetopause and ring current and regions in between; as well as to associated  ground signatures, driven by the external conditions. Large and small-scale (MLT) trends in FAC orientation can be inferred from dual-spacecraft (e.g. the Swarm A&C spacecraft). Conjugate effects seen in ground (dH/dt), ionospheric and magnetospheric magnetic signals show that intense, coherent FA currents can take place in the polar cusp during the main phase of a geomagnetic storm and at near tail local times during substorm activity, at different altitudes. In the former event the mesoscale FACs show vertical scaling and a corresponding geomagnetic disturbance, driven by unsteady magnetic reconnection at the magnetopause. In the latter event, the most intense dH/dt is shown to be associated with FACs driven into the ionosphere by the arrival of bursty bulk flows BBFs at geosynchronous orbit (linked via a modified sub-storm current wedge, SCW). In situ ring current morphology can also be investigated by MMS, THEMIS and Cluster during the Swarm era, and can be compared to distributions of R2-FACs.
MOBO-DIC: Recent Trends and Variability in the Oceanic Storage of Dissolved Inorganic Carbon
Authors: Keppler Lydia , Landschützer Peter , Lauvset Siv K. , Gruber Nicolas
Published: February 2023

Several methods have been developed to quantify the oceanic accumulation of anthropogenic carbon dioxide (CO2) in response to rising atmospheric CO2. Yet, we still lack a corresponding estimate of the changes in the total oceanic stock of dissolved inorganic carbon (DIC). In addition to the increase in anthropogenic CO2, changes in DIC also include any alterations of the natural CO2 pool. Once integrated globally, changes in DIC reflect the net oceanic sink for atmospheric CO2, complementary to estimates of the air-sea CO2 exchange based on surface measurements. Here, we extend the machine learning approach by Keppler et al. (2020) to estimate global monthly fields of Mapped Observation-Based Oceanic DIC (MOBO-DIC) at 1° resolution over the top 1500 m from January 2004 through December 2019. We find that over these 16 years and extrapolated to cover the whole global ocean down to 4000 m, the oceanic DIC pool increased close to linearly at an average rate of 3.2±0.7 Pg C yr-1. This trend is statistically indistinguishable from current estimates of the oceanic uptake of anthropogenic CO2 over the same period. Thus, our study implies no detectable net loss or gain of natural CO2 by the ocean, albeit the large uncertainties could be masking it. Our reconstructions suggest substantial internal redistributions of the natural oceanic CO2 pool, with a shift from the mid-latitudes to the tropics and from the surface to below ~200 m. Such redistributions correspond with the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation. The interannual variability of DIC is strongest in the tropical Western Pacific, consistent with the El Niño Southern Oscillation. Reference:Keppler, L., Landschützer, P., Gruber, N., Lauvset, S. K., & Stemmler, I. (2020). Seasonal carbon dynamics in the near-global ocean. Global Biogeochemical Cycles, 34, e2020GB006571. https://doi.org/10.1029/2020GB00657
ESM Data Exploration with the Model Data Explorer
Authors: Sommer Philipp S. , Baldewein Linda , Takyar Hatef , Chaudhary Rehan , Hadizadeh Mostafa , Dibeh Housam , Böcke Max , Lorenz Christof , Dinter Tilman , Pinkernell Stefan , Getzlaff Klaus , Kleeberg Ulrike
Published: February 2023

Making Earth-System-Model (ESM) Data accessible is challenging due to the large amount of data that we are facing in this realm. The upload is time-consuming, expensive, technically complex, and every institution has their own procedures.Non-ESM experts face a lot of problems and pure data portals are hardly usable for inter- and trans-disciplinary communication of ESM data and findings, as this level of accessibility often requires specialized web or computing services. With the Model Data Explorer, we want to simplify the generation of web services from ESM data, and we provide a framework that allows us to make the raw model data accessible to non-ESM experts.Our decentralized framework implements the possibility for an efficient remote processing of distributed ESM data. Users interface with an intuitive map-based front-end to compute spatial or temporal aggregations, or select regions to download the data. The data generators (i.e. the scientist with access to the raw data) use a light-weight and secure python library based on the Data Analytics Software Framework (DASF, https://digital-earth.pages.geomar.de/dasf/dasf-messaging-python) to create a back-end module. This back-end module runs close to the data, e.g. on the HPC-resource where the data is stored. Upon request, the module generates and provides the required data for the users in the web front-end.Our approach is intended for scientists and scientific usage! We aim for a framework where web-based communication of model-driven data science can be maintained by the scientific community. The Model Data Explorer ensures fair reward for the scientific work and adherence to the FAIR principles without too much overhead and loss in scientific accuracy. The Model Data Explorer is in the progress of development at the Helmholtz-Zentrum Hereon, together with multiple scientific and data management partners in other German research centers. The full list of contributors is constantly updated and can be accessed at https://model-data-explorer.readthedocs.io.
EGU (European Geosciences Union) Field Officer Programme In Türkiye
Authors: Kafalı Candan
Published: February 2023

In our century, considering the increasing environmental problems, natural disasters, the raw materials of indispensable products we use in daily life are obtained from nature and natural destructions are considered, geoscience education is very important for countries. The main purpose of geoscience education is to enable individuals to have knowledge of natural processes and to understand how human activities affect the Earth. One of the most important stages of geoscience education is to ensure the professional development of teachers. For this purpose, EGU (European Geosciences Union) Committee on Education (CoE)  has started the Geoscience Education Field Officer Programme and within the scope of this program, the geoscience workshops held in Türkiye. The aim of the workshops is to provide the professional development of teachers who have geoscience elements in their curriculum with interactive activities.Workshops that develop critical thinking and creativity and enable participants to learn from their own experiences are planned for two hours. Workshop activities are planned as interactive and hand on group work and consist of easily accessible and simple materials. After the workshops, data is collected with the evaluation form provided by EGU. According to the evaluation form, it is seen that the participants showed a high level of interest in the workshop.Participating teachers declared that the hands-on activities in the workshop were experiments and activities that could be carried out with easily accessible materials that could attract the attention of students and enable them to learn more effectively by doing / experiencing geosciences. 84 percent of the teachers wanted such workshops to be held more frequently in the future. The collected data confirms that the geocience Education Workshop in Türkiye has a positive impact on the participating teachers. This finding is supported by the teachers' high level of interest in the workshop, expressing the workshop as a useful and enjoyable environment where they learn from their own experiences, and they plan to attend future workshops.
CO2 hazard mapping in the buildings of La Bombilla and Puerto Naos (La Palma, Canary Islands) using of alkaline traps
Authors: Rodríguez Noelia , Asensio-Ramos María , Melián Gladys V. , Amonte Cecilia , Álvarez Díaz Antonio J. , González Pérez Alexis M. , Rodríguez Fátima , Padilla Germán D. , Barrancos José , D'Auria Luca , Hernández Pedro A. , Pérez Nemesio M.
Published: February 2023

Carbon dioxide (CO2) is a gas that is found in small amounts in Earth's atmosphere. It is released into the atmosphere by volcanoes, but it is typically diluted to low concentrations very quickly and is not harmful to people or animals. However, in certain stable atmospheric conditions, cold CO2 gas can flow into low-lying areas and reach higher concentrations. At these concentrations, CO2 can be dangerous. If the air contains more than 3% CO2, it can cause symptoms such as headaches, dizziness, increased heart rate, and difficulty breathing. At concentrations above 15%, CO2 can quichly lead to unconsciousness and death. The boundary between healthy air and lethal gas can be extremely sharp; even a single step upslope may be adequate to escape death.Since the Tajogaite eruption ended on December 13, 2021, high concentrations of CO2 up to 20% have been observed inside the buildings of La Bombilla and Puerto Naos (La Palma, Canary Islands).  Anomalous concentrations of CO2 are detected mainly in the ground-floor and basement of the buildings in Puerto Naos, and their distribution is not homogeneous or uniform throughout the Puerto Naos area (Hernández P.A. et al, 2022).  Because of the ultradilute nature of CO2 in the atmosphere, chemical sorbents with strong CO2-binding affinities are typically employed for CO2 capture. For this study, we set up a network of 45 alkaline traps stations located on the ground-floor (streel level) inside buildings of Puerto Naos. These chemical sorbents were made up KOH 1.2 M and replace weekly. The aim of this study was to distinguish between indoor areas that had high levels of CO2 in their alkaline traps and those with lower levels of CO2. This was done through weekly surveys conducted from May to October 2022 in order to identify and define areas with the greatest CO2 hazard. The statistical analysis of the results shows that 75% of the variance of the amounts of CO2 retained weekly in the alkaline traps seems to be related to an endogenous CO2 source while 25% of the variance observed is related to external variables. Hernández, P. A., Padrón, E., Melián, G. V., Pérez, N. M., Padilla, G., Asensio-Ramos, M., Di Nardo, D., Barrancos, J., Pacheco, J. M., and Smit, M.: Gas hazard assessment at Puerto Naos and La Bombilla inhabited areas, Cumbre Vieja volcano, La Palma, Canary Islands, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7705, https://doi.org/10.5194/egusphere-egu22-7705, 2022.
Observed Global Photosynthesis Response to Changing Storm Frequency and Magnitude
Authors: Feldman Andrew , Poulter Benjamin , Joiner Joanna , Asadollahi Mitra , Biederman Joel , Chatterjee Abhishek , Gentine Pierre , Konings Alexandra , Smith William , Wang Lixin
Published: February 2023

Rain events are becoming less frequent, but stronger in many global locations under a changing climate. These intra-seasonal rainfall features have received less attention than changes in mean temperature and total annual rainfall in their influence on the global carbon cycle. Field rainfall manipulation experiments consistently show non-negligible changes to annual photosynthesis in response to rainfall frequency alterations while holding total annual rainfall constant. However, field and modeling experiments show little consensus on the sign and magnitude of change of annual photosynthesis due to changing storm frequency and magnitude. In this study, we ask: based on satellite observations, how is global photosynthesis changing due to shifts in storm frequency and magnitude? What are the soil-plant-atmosphere drivers of the response?Using several global satellite-based photosynthesis proxies, we find that the annual photosynthesis response to storm frequency is as high in magnitude and global spatial extent as its response to total annual rainfall. The satellite-based photosynthesis proxies and field tower sites indicate that years with fewer, stronger storms tend to show decreased photosynthesis in humid ecosystems and increased photosynthesis in drylands. The absolute magnitudes of annual photosynthesis trends show 10-20% per century changes due to rainfall frequency trends over nearly half of vegetated surfaces, which is consistent with the magnitude and extent of total annual rainfall trend effects. The contrasting responses observed in humid locations and drylands are shown to be driven by patterns of plant pulse response, soil texture, and mean atmospheric aridity response to rain frequency. Ultimately, our results indicate that intra-seasonal rainfall variability drives global photosynthesis interannual variability similarly to interannual rainfall variability.
AI combined with Flux Data and Remote sensing generates insightful carbon footprint data for the land-use sector
Authors: Demidov Oleg
Published: February 2023

The Agriculture, Forestry, and Other Land Use (AFOLU) sector accounts for almost a quarter of global emissions. With proper management, the land-use sector can serve as a powerful carbon sink through sustainable practices along agriculture and forestry supply chains and through nature-based solutions (NBS) for carbon removal. However, accounting for the exchange of CO2 due to biological processes (soils, crops, trees, livestock) in the land is difficult, which leads to uncertainty around the exact impact of carbon removal practices and projects.Current methods for measuring CO2 emissions and sequestration in the land have gaps. Direct measurements (e.g. soil sampling) are too costly and labor intensive. Industry calculators provide averaged data, which masks local variation and individual effort. Remote-sensing solutions focus on detecting the change of select practices but still use standardized formulas to estimate the carbon impact.These factors limit the ability of companies to track and incentivize the sustainable practices of their suppliers, mislead climate action, and slow down the transition to Net Zero.In this talk, Dr. Oleg Demidov will discuss the CarbonSpace technology, which provides unique, remotely generated carbon footprint data and insights for global supply chains and NBS projects. The technology core uses machine learning algorithms trained on multispectral satellite imagery and GHG flux data from globally distributed eddy covariance ground stations. This approach requires no on-site operations and provides net ecosystem exchange (NEE) estimates at a resolution of 30 meters. NEE is a parameter that represents carbon stock change in several carbon pools: aboveground biomass, belowground biomass, soils, and dead organic matter. NEE can be positive or negative, meaning total emissions or sequestration. Dr. Demidov will cover several case studies demonstrating the proven value of CarbonSpace data. In North Dakota, CarbonSpace showed the impact of varied management practices on carbon sequestration in croplands, and, in Ireland, CarbonSpace provided a new set of data that improved the accuracy of a dairy product LCA. Additionally, Dr. Demidov will discuss progress and challenges with certification and market acceptance for the CarbonSpace technology. Join this discussion to learn how CarbonSpace’s disruptive approach enables corporations and NBS project developers to evaluate their carbon removal efforts and guide further climate strategy based on quality, accurate data.
Highlights of the ACCLIP Campaign 2022: Operations and Science
Authors: Pan Laura , Newman Paul , Atlas Elliot , Thornberry Troy , Randel Bill , Toon Brian
Published: February 2023

The Asian summer monsoon Chemical and Climate Impacts Project (ACCLIP) is a large airborne field campaign conducted over the Western Pacific in summer 2022. The campaign deployed two research aircraft, the NCAR Gulfstream V (GV) and the NASA WB-57, both with extensive payload of chemistry and microphysics measurements for trace gas and aerosol content to investigate the Asian monsoon convective outflow at the UTLS levels. The campaign also included ground-based balloon soundings and extensive collaborative measurements in the region. Based from Osan, Republic of Korea, a total of 29 research fights were conducted covering the vertical range of 300 ft above sea level to ~70 hPa over a large domain of western Pacific (15°N-43°N, 125°E-155°). These measurements provide novel information on the role of Asian summer monsoon in altering atmospheric composition, serving as a distinct linkage between the weather and the climate through its impact on ozone chemistry and aerosol radiative effect. Observational highlights and initial indications of post campaign data analysis and modeling will be presented in this overview.