bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the performance of hydrological model LISFLOOD using the ECMWF seasonal meteorological forecast at 1arcmin-1day spatiotemporal resolution over German catchments
Authors: Espitia Espitia Edgar Fabian , Diaz Esteban Yanet , Heidari Fatemeh , Lin Qing , Xoplaki Elena
Published: March 2024

Floods and their devastating effects on society and economy have increased dramatically in Germany, and Europe in recent years. At the end of 2023, rivers and streams across Germany burst their banks due to heavy rainfall, affecting property, transport and power supplies and necessitating rescue operations and evacuations to protect human lives. One measure to deal with flooding and safeguard lives and property is the implementation of early warning systems, such as the European Flood Awareness System (EFAS), which provides short-term hydrological forecasts in real time. However, preparedness is essential along the responders value chain and longer term forecasts are important to anticipate, take precautions, raise awareness and generally mitigate the effects of flooding. The objective of this study is to evaluate the performance of hydrological forecasting using the seasonal meteorological forecast at a spatio-temporal resolution of 1 arcmin and day over Germany including all transboundary catchments for the period from 1990 to 2020. The hydrological model used was LISFLOOD. In the first step, LISFLOOD was calibrated using the meteorological observations, the EMO 1arcmin dataset and the discharge data from the transnational hydrological portal for all federal states and neighboring countries. The characteristics of land use, land cover, soil, groundwater, and human activity referred to as surface fields for global environmental modelling, were provided by EFAS. The second step, downscaling of the seasonal (long-term) forecast meteorological forcing to 1arcmin, is performed using a Deep Residual Neural Network (DRNN), and a bilinear interpolation approach over the seasonal forecast information of atmospheric conditions up to seven months into the future provided by the European Center for Medium-Range Weather Forecasts (ECMWF), 25 ensemble members in total. In the third step, the discharge is simulated by feeding the LISFLOOD model with two meteorological forcing scenarios, the DRNN downscaled and the bilinear approach of the seasonal meteorological forecast, to finally compare the performance with the observed runoff using the modified Kling-Gupta efficiency criteria (KGE'). The calibrated and validated LISFLOOD parameters showed a good and acceptable performance in all catchments, KGE' between 0.6 and 0.9. The DRNN downscaling technique shows a promising result, providing a good agreement between downscaled and observed dataset. Finally, the hydrological performance, KGE', is expected to be improved by 0.05 to 1 in the hydrological stations with good and poor performance, respectively, by using the DRNN downscaled seasonal forecast.
Representation of dynamic grass density in land surface model ORCHIDEE trunk v4.
Authors: Xu Siqing , Balkanski Yves , Luyssaert Sebastiaan , Ciais Philippe , Sciare Jean
Published: March 2024

In semi-arid regions, grasslands naturally display a self-organized pattern that optimizes resource utilization and productivity. Representing this type of vegetation in land surface model constitutes a difficult challenge. To simulate these grasses, the ORCHIDEE land surface model treats grass density as the ratio of the area occupied by individuals to the Plant Functional Type (PFT) area, assuming a fixed grass density of 1 for maximal occupancy. However, the fixed maximal grass density lacks the response of grassland to environmental perturbations. In addition, the low biomass contained in certain pixels results in frequent mortality, indicative of resource limitations at the plant individual level. To address this considerable limitation, we introduced dynamic reduction of grass density based on mortality indicators, hence enhancing individual biomass and alleviating mortality occurrences. The adaptive approach significantly decreased mortality events across most pixels while enhancing leaf area index (LAI) for the majority of them. Our findings suggest that optimizing resource through grass density reduction in response to environmental condition, could not only improve individual biomass to alleviate mortality but also enhance overall grassland production.
Snow on permafrost: the effect of spatial snow variability on soil temperature in Trail Valley Creek, NWT, Canada
Authors: Grünberg Inge , Hollenbach Borges Daniela , Hammar Jennika , Rutter Nick , Marsh Philip , Boike Julia
Published: March 2024

Snow is a potent insulator, influencing the temperature of the active layer and the permafrost in the Arctic region. However, our understanding of spatial patterns of snow properties and their interplay with vegetation remains limited due to scarcity of local and regional snow data. Furthermore, the duration, depth, and physical properties of the Arctic snow cover are changing with rising air temperature and new precipitation patterns. We study the spatial snow distribution and its drivers and consequences around the Trail Valley Creek research catchment in the Northwest Territories, Canada. Our dataset includes a 143 km² snow depth raster captured on April 2, 2023, at a 1-meter spatial resolution, as well as data from 13 spatially distributed loggers measuring air/snow temperature, soil surface temperature, and soil temperature at 8 cm depth from August 27, 2022, to August 9, 2023. Detailed information on vegetation types, structure, and soil properties at all locations is included. Our analysis covers the timing of soil freeze and thaw, snow and soil temperatures, and their correlation with vegetation characteristics, particularly focusing on April snow depth. Our findings underscore the pivotal role of snow in regulating soil temperature, making it a key driver for permafrost protection or thaw. The results reveal significant variability in April snow depth across the 13 study locations, ranging from no snow to 1.7 meters, resulting in winter minimum soil temperatures between -31°C and -4°C. The study confirms that thicker snow cover contributes to warmer soil temperatures. While the soil at 8 cm freezes uniformly in mid-October across all sites, snow patterns lead to high variability in soil thawing dates, which span one month between May 10 and June 08, 2023. Understanding the spatial patterns of snow depth, thermal properties, and timing is crucial for assessing the snow effect on soil temperature. The large range of winter soil temperatures, which we observed, may lead to differences in thaw depth development in the following summer and potentially to talik formation affecting permafrost stability.
Aerosol size distribution: One year of measurements and retrieval procedures comparison
Authors: Bruchkouski Ilya , Szkop Artur , Pietruchuk Aleksander
Published: March 2024

Aerosol size distribution (ASD) is the most important physical characteristic of aerosols, which determines the extent of aerosol penetration into a human’s respiratory tract: how particles are inhaled and where they deposit within the respiratory system. Therefore, correct measurements of aerosol size distribution are essential for assessing their potential impact on human health. Also, information about ASD is important for many different scientific applications, including radiative transfer models, source identification, air quality monitoring, optical remote sensing, atmospheric correction procedures, aerosol-cloud interactions, environmental impact assessment and climate studies.The retrieval of ASD from a diverse range of optical observations can be accomplished using GRASP (Generalized Retrieval of Atmosphere and Surface Properties) [1]. This study focuses on employing in-situ nephelometer measurements for ASD retrieval through the GRASP technique. To ensure the accurate processing of nephelometer data by the GRASP technique, it is imperative to determine appropriate model settings. The primary objective of this work is to determine the optimal GRASP settings for harmonizing a one-year series of in-situ aerosol measurements. The significance of this research lies in the developing of a multi-instrumental approach aimed at determining a correct model configuration, applicable to aerosol scattering measurements under diverse meteorological conditions.In-situ one-year measurements were conducted at the suburban measurement site in Racibórz (50 °E, 18 °N), utilizing the Aurora 4000, Aerodynamic Particle Sizer (APS) and Scanning Mobility Particle Sizer (SMPS) spectrometers. The Aurora 4000 is an integrating nephelometer that can measure light scattering in a sample of ambient air at three wavelengths: 450, 525, and 635 nm in several angular sectors from 10° to 90° through to 170°. Aerosol scattering measurements in 17 different angular ranges served as input for the GRASP algorithm, which was employed to obtain the size distribution of fine and coarse aerosol modes separately. The GRASP retrievals of aerosol size distribution were subsequently compared to measurements taken by both APS and SMPS instruments.A one-year series of data allows for the comparison of relatively long time series and the testing of different settings of the software and its performance. Special attention was given to finding best model settings under different meteorological conditions. This work was supported by the National Science Centre under grant 2021/41/B/ST10/03660.[1] Moallemi, A., Modini, R. L., Lapyonok, T., Lopatin, A., Fuertes, D., Dubovik, O., Giaccari, P., and Gysel-Beer, M.: Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data, Atmos. Meas. Tech., 15, 5619–5642, 2022. https://doi.org/10.5194/amt-15-5619-2022 
Thermal porosity waves
Authors: Räss Ludovic , Utkin Ivan , Podladchikov Yuri
Published: March 2024

Fast vertical fluid transfers in the crust play a crucial role in transporting elements and energy from deep environments into the shallow subsurface. These fluid transfers also impact magmatic processes, metamorphism, and heat distribution. Heat distribution in the subsurface is key for temperature-dependent geological processes, geothermal energy, and reservoir operations. Therefore, assessing the efficiency of heat transport by localised fluid flow is important.Nonlinear porosity waves, resulting from hydro-mechanical interactions, provide a mechanism for fast vertical fluid transfers in the subsurface. However, their ability to transport heat has not been fully explored yet.In this study, we investigate the coupling of hydro-mechanical processes with thermal processes to assess the efficiency of heat transport by porosity waves in a porous subsurface environment. We use numerical simulations to solve the coupled thermo-hydro-mechanical equations and present high-resolution modeling results. We also evaluate the role of a consistent and conservative formulation. Our preliminary findings suggest that porosity waves do not significantly enhance heat transfer in the subsurface. Additionally, we discuss the influence of parameters such as porosity, permeability, and fluid properties on the efficiency of heat transport.
Greenhouse gas fluxes monitored for 5 years in two drained boreal peatlands using eddy covariance and automatic light-dark chambers
Authors: Larsen Poul , Ibrom Andreas , Pirk Norbert , Jørgensen Preben , Larsen Klaus Steenberg
Published: March 2024

Peatlands store large amounts of organic carbon, which become subject to increased microbial decomposition and mineralization to primarily CO2 upon drainage. Drained peatlands are often characterized by horizontal variability in soil water content and saturation, with drier parts closer to drainage ditches. This causes spatial heterogeneity in greenhouse gas fluxes, where CH4 emissions are higher in the wetter areas, while respiratory CO2 production dominates in drier areas.
We investigated two neighboring, drained ombrotrophic bogs in Norway close to Trysil, Innlandet, 61.1N- 12.25E, 640 m a. s. l. We used a total of 10 automated, ecosystem-level, light-dark chambers (ECOFluX) to examine the seasonality of CO2, CH4 and N2O fluxes at different microsites along the water table gradient from the center of drained patches to the drainage ditches to relate GHG fluxes to small scale spatial heterogeneity. With eddy covariance measurements of CO2 and CH4 fluxes, we estimated GHG fluxes over representative areas of each bog. In year 3, the natural hydrology of one of the sites (South) was restored while the other site (North) was kept as a drained reference site. We here present a comparative analysis of the five years of measurements, where we examine shifting spatial patterns of GHG production at different scales and relate them to soil water conditions.
The automated chambers (five chambers within each footprint of each eddy flux tower) showed higher spatial variability for CH4 fluxes than for CO2 with higher CH4 emissions in the wetter plots furthest away from ditches, i.e. CH4 fluxes correlated well with the water table depth at both sites. Very small N2O emissions were observed only during very short events in the early summer of year 1.
While the CO2 fluxes were highly comparable at the two investigated sites, the CH4 fluxes were moderately higher at the lower and wetter of the two sites (North). At both sites, the CH4 fluxes correlated well with local variations in groundwater table and overall there was a good alignment of fluxes measured with eddy flux and chamber technologies. Restoring the south site resulted (on average) in 6-10 cm elevation of the ground water table, but in an increase of CH4 emissions of approx. 70%. At the restored site, we observed a decrease in ecosystem respiration and photosynthesis rates compared to the reference site during the first year after restoration. Finally, we will present the overall conclusions of the 5 year study and our recommendations for the stakeholders.
Inherited glacier structures influence glacial lake dam morphology
Authors: Glasser Neil , Peacey Matt , Reynolds John , Holt Tom , Hepburn Adam
Published: March 2024

We investigate the influence of inherited glacier structures on the development of moraine dam morphology over 61 years on four glacial lake dams in the Eastern Himalaya. We compare glacier structures from 1962 Corona imagery with current dam features from 2023 Maxar imagery at Imja, West Barun, Melung and Dang Pu glaciers. From the Corona imagery, maximum glacier extents were identified, along with discrete flow units, ice cliffs, transverse structures, and supraglacial ponds at a 2.5 m resolution. In the Maxar imagery, key dam components were identified including extent of dead ice, hummocky moraines, thermokarst, and surface drainage features and surface structures, mapped at 1 m resolution. Our analysis revealed that former glacier flow unit boundary locations coincide with the development of hydrological features on the dam surface. This was augmented with further analysis of surface elevation change, thermokarst, and surface drainage development using historical aerial imagery, satellite imagery, and DEMs for Imja from 1962 to 2023. We propose that hydrological features exploit relict flow unit boundaries and conclude that inherited glacier structures are key in understanding the development of lake dams that contain dead ice. The glaciological influences on dam features should be included in integrated hazard assessments in glacial settings.
Ice- Ocean- Atmosphere Interactions in the Arctic: Glaciers and Ice Caps
Authors: Fotheringham Morag , Gourmelen Noel , Tsamados Michel , Slater Donald
Published: March 2024

Arctic glaciers and ice caps are currently major contributors to global sea level rise, with future projections showing a sustained input. The monitoring of these smaller land-ice masses is challenging due to the high temporal and spatial resolution required.These glaciers and ice caps are losing mass in response to climate forcings, both atmospheric and oceanic. The relative significance of these forcings is currently unknown with most recent catagorisation focusing on separating loss caused by internal dynamics vs surface mass balance changes.This leaves the specific roles of the atmosphere and the ocean unconstrained; this understanding is key to improving the accuracy of future loss of ice from these smaller land-ice masses and future sea level rise projections.This study uses CryoSAT-2 swath interferometric radar altimetry to provide high spatial and temporal observations to produce elevation timeseries in order to evaluate the trends of mass loss. It also utilises an ocean thermal model, previously used to study Greenland's outlet glaciers, to gain a better understanding of the relative contributions of atmospheric and ocean forcings to this mass loss.
Can reservoirs and dams effectively reduce flood runoff in river basins? A case study of the Rhine basin
Authors: Merz Ralf , Speckhann Gustavo Andrei , Nguyen Viet Dung , Merz Bruno
Published: March 2024

Flood retention basins constitute a pivotal component of flood protection measures. Local studies have unequivocally demonstrated their efficacy in significantly mitigating flood discharges, thereby minimizing potential downstream damage. However, the impact of these retention basins on the reduction of flood discharges at the large river basin scale remains ambiguous.This study delves into the assessment of the influence wielded by reservoirs and dams on the reduction of flood discharges within the Rhine basin. Employing a spatially distributed version of the HBV model and Nash-cascade routing, daily discharges from 912 sub-catchments spanning the period 1951-2020 were simulated. The modeling approach comprehensively incorporates the influence of 192 reservoirs in the Rhine catchment on daily runoff volumes. Calibration at 200 gauging stations, facilitates a regional parameterization of the model, based on the PASS method.Through various scenarios, the study explores how large-scale flood discharges would evolve in the absence of reserves for flood protection or if there were alterations to the storage capacity and function of individual reservoirs. Beyond merely assessing the reduction of runoff peaks, the research scrutinizes alterations in the duration of individual flood events and their spatial expansion, taking into account the intricate network of the 192 reservoirs.In essence, this study not only contributes to the ongoing discourse on the efficacy of flood retention basins but also sheds light on the nuanced dynamics of reservoirs and dams in shaping the hydrological landscape of the Rhine basin. The findings provide valuable insights for optimizing flood protection strategies, encompassing considerations of storage capacities, operational functions, and the broader spatial and temporal dimensions of flood events.
NIMS/KMA Plans for Climate Change Projection Production and Utilization on CMIP7
Authors: Chung Chu-Yong , Byun Young-Hwa , Sung Hyun Min , Kim Jin-Uk , Shim Sungbo
Published: March 2024

The National Institute of Meteorological Sciences in the Korea Meteorological Administration (NIMS/KMA) has been actively contributing to the CMIP program since CMIP3. NIMS participated in CMIP6 through a collaborative effort with the UK Met Office Hadley Centre as part of a mutually agreed scientific plan. Within this collaboration, NIMS utilized the Earth System Model developed by the UK Met Office (UKESM) to generate future climate change scenarios for four distinct Shared Socio-economic Pathways (SSPs). NIMS also employed the KMA Advanced Community Earth (K-ACE) model, a modified version of HadGEM2-AO developed through in-house research, to analyze global climate projections. Five different regional climate models were used for the regional climate simulations: HadGEM3-RA, RegCM4, CCLM, GRIMs, and WRF, organized under the CORDEX-EA (East Asia) program. Furthermore, for the South Korean area, NIMS produced 1km resolution climate change scenario data using the statistical downscaling technique, the Parameter-elevation Relationships on Independent Slopes Model (PRISM)-based Dynamic downscaling Error correction (PRIDE). These projections played a pivotal role in contributing to the preparation of the Sixth Assessment Report (AR6) by the Intergovernmental Panel on Climate Change (IPCC) and provided crucial foundational data for national climate change adaptation efforts.Currently, NIMS has initiated preparations for CMIP7 participation. In this program, K-ACE will be employed for producing global climate projections, having undergone improvements such as coupling with an ocean-biogeochemistry model, TOPAZ, and modifications to the cloud-aerosol process, among other enhancements. NIMS plans to use a reduced number of RCMs compared to the CMIP6 phase but intends to increase the ensemble members by combining physical processes. Currently under consideration as RCM candidates are WRF and WRF-ROMS. To comprehend the impact of climate change on local-scale heavy rain, a Convection Permitting Model (CPM) with a spatial resolution of about 2.5km can be employed. For the South Korean region, our objective is to produce more high-resolution, detailed climate scenarios through sensitivity experiments and reliability verification studies.This presentation aims to introduce KMA's Earth System Models, aligning with recent trends and developments outlined in CMIP7, and presenting the overall plans for the generation and utilization of global-regional-local climate projections in line with CMIP7.
Development of a non-targeted LC-UHRMS approach for organic aerosol analysis: first application to urban and biogenically influenced air masses
Authors: Karbach Niklas , Pouyes Pauline , Perraudin Emilie , Villenave Eric , Vogel Alexander , Hoffmann Thorsten
Published: March 2024

The analysis of filter samples of atmospheric organic aerosols provides information about atmospheric processes and the origin of aerosol particles. However, limiting analysis to a few target compounds ignores a large proportion of the compounds present on the filters. Since the availability of high-resolution mass spectrometers, non-target analysis addresses parts of this problem. However, such an analysis can be very time-consuming. Since it is hardly possible to analyze all individual compounds manually, an automated or semi-automated evaluation of the data is required.This poster presents a method for the non-target analysis of atmospheric organic aerosol filter samples using UHPLC-Orbitrap-MS. The extracted filter samples are analyzed in a two-step process that provides maximum information. In the first step, a full-scan high resolution mass spectrum is measured, which is then analyzed with MZmine, capturing all compounds with their respective retention time and exact mass. Using this data, a second experiment is designed in which an isolated MS/MS spectrum (with stepped fragmentation energy) of all detected compounds is measured. With the MS/MS data of the measured compounds and a local database in combination with in-silico fragmentation, a reliable prediction of the chemical composition, functional groups and/or parts of the molecular structure is possible. The combination of these steps drastically improves the reliability of the prediction, as not only the exact mass of the molecule is considered, but also additional information about the fragmentation of the molecule is included. Python scripts automate the processes and create a comprehensible summary for each detected compound, minimizing the manual workload.For this contribution, filters of the ACROSS campaign 2022 (Rambouillet Forest, France), where urban and biogenically influenced air masses are present, were analyzed in the manner described above and a brief summary of the results is given.
AIFS – ECMWF’s Data-Driven Probabilistic Forecasting 
Authors: Ben Bouallegue Zied , Alexe Mihai , Chantry Matthew , Clare Mariana , Dramsch Jesper , Lang Simon , Lessig Christian , Magnusson Linus , Prieto Nemesio Ana , Pinault Florian , Raoult Baudouin , Tietsche Steffen
Published: March 2024

In just two years, the idea of an operational data-driven system for medium-range weather forecasting has been transformed from dream to very real possibility. This has occurred through a series of publications from innovators, which have rapidly improved deterministic forecast skill. Our own evaluation confirms that these forecasts have comparable deterministic skill to NWP models across a range of variables. However, on medium-range timescales probabilistic forecasting, typically achieved through ensembles, is key for providing actionable insights to users. ECMWF is building on top of these recent works to develop a probabilistic forecasting system, AIFS. We will showcase results from our progress towards this system and outline our roadmap to operationalisation.
Utilizing Geographic Information Systems to Identify and Map Climate Hazards in Greece: A Regional Analysis
Authors: Valvi Kleoniki , Cartalis Constantinos , Philippopoulos Kostas , Zazani Athina-Kyriaki , Agathangelidis Ilias
Published: March 2024

The aim of the present study is the identification of the prevailing climate hazards (e.g., extreme heat, forest fires, drought, floods) and their changes, in terms of frequency, intensity, and trends during multiple 30-year climate reference periods in Greece. The analysis involves the identification of climate hazards using a plethora of extreme event indices along with the application of the extreme value theory (EVT). Changes in extremes over a period are often examined under two different perspectives, one that detects changes in the frequency of the extremes and the other in their intensity. For this purpose, high-resolution reanalyses data (ERA5-Land) are used, with a horizontal resolution of 0.1o x 0.1o. The sensitivity of diverse regions was determined through the analysis of Earth Observation data and products, alongside with the examination of their geomorphological features. In the final stage of the work, all of the above were incorporated using Geographic Information Systems, and GIS tools were developed for the synthesis of the climate hazards. This analysis focuses on the understanding of how climate change may be impacting Greece and can provide valuable insights for policymakers, researchers, and the general public to adapt to and mitigate the effects of climate hazards on a regional scale.
Pattern Recognition of Convection in the Atlantic Intertropical Convergence Zone
Authors: Hayo Lennéa , Windmiller Julia , Schulz Hauke , Acquistapace Claudia , Crewell Susanne
Published: March 2024

The Intertropical Convergence Zone (ITCZ) in the Atlantic is typically described as a narrow band of precipitation and deep convection. However, this description often stems from long-term averaging of precipitation or outgoing longwave radiation in the ITCZ. On shorter time scales, the ITCZ is much more dynamic and various classifications of different patterns can be attempted. One possibility is based on satellite images collected during the GATE campaign in 1974 where four patterns - Line, Double Line, Broad and Cluster - were previously identified. In our analysis, we build on the pioneering results from GATE, supplement the category No Clouds, and validate the patterns based on 43 years of harmonized, equal-angle grid geostationary satellite images. In a first attempt, manual classification of these patterns in the visible spectrum proved feasible for 1000 km wide cutouts and for manually defining the extent of the pattern on the entire Atlantic ITCZ. Manual classification for July 2021 has already shown that all classes neither occur with the same frequency nor the same spatial ditribution over all regions of the Atlantic. For further analysis on the appearance of these patterns on longer time scales the satellite images have been classified by a machine learning algorithm, and their frequency dependence on season and region have been analyzed. These results now enable us to ask why these different patterns occur.
Towards a Deep Learning Approach for Data-Driven Short-Term Spatiotemporal Earthquake Forecasting 
Authors: Dervisi Foteini , Segou Margarita , Baptie Brian , Main Ian , Curtis Andrew
Published: March 2024

The development of novel deep learning-based earthquake monitoring workflows has led to a rapid increase in the availability of earthquake catalogue data. Earthquake catalogues are now being created by deep learning algorithms at significantly reduced processing times compared to catalogues built by human analysts and contain at least a factor of ten more earthquakes. The use of these rich catalogues has been shown to have led to improvements in the predictive power of statistical and physics-based forecasts. Combined with the increasing availability of computational power, which has greatly contributed to the recent breakthrough in the field of artificial intelligence, the use of rich datasets paired with machine learning workflows seems to be a promising approach to uncovering novel insights about earthquake sequences and discovering previously undetected relationships within earthquake catalogues.
Our focus is on employing deep learning architectures to produce high-quality earthquake forecasts. Our hypothesis is that deep neural networks are able to uncover underlying patterns within rich earthquake catalogue datasets and produce accurate forecasts of earthquakes, provided that a representative dataset that accurately reflects the properties of earthquake sequences is used for training. We use earthquake catalogue data from different geographical regions to build a time series of spatiotemporal maps of past seismicity. We then split this time series into training, validation, and test datasets in order to explore the ability of deep neural networks to capture patterns within sequences of seismicity maps and produce short-term spatiotemporal earthquake forecasts.
We assess the performance of the trained deep learning-based forecasting models by using metrics from the machine learning and time-series forecasting domains. We compare the trained models against a null hypothesis, the persistence model, which assumes no change between consecutive time steps and is commonly used as a baseline in various time series forecasting settings. The persistence null hypothesis has been proven to be a very effective model due to the fact that when only background seismicity is observed, there is very little change between consecutive time steps. We also evaluate the relative performance of different deep learning architectures and assess their suitability for dealing with our specific problem. We conclude that deep learning techniques are a promising alternative to disciplinary statistics and physics-based earthquake forecasting methods as, once trained, deep learning models have the potential of producing high-quality short-term earthquake forecasts within seconds. This realisation can influence the future of operational earthquake forecasting and earthquake predictability. 
Ice nucleating properties of air filter and snow samples taken in the Central Arctic during MOSAiC
Authors: Kirchgaessner Amélie , Frey Markus , van den Heuvel Floor , Lachlan-Cope Tom , Ranjithkumar Ananth , Yang Xin
Published: March 2024

Arctic clouds are still poorly represented in climate models. An important reason for this is our lack of knowledge regarding the various sources of natural aerosol in the high Arctic. Recent field campaigns have provided evidence that over sea ice blowing snow can act as a source of sea salt aerosol (SSA). This source can account for the maximum in SSA that occurs in the Polar Regions during winter and spring. SSA can influence the regional climate through the indirect radiative effect, but also through the role it plays as nucleation particle in cloud formation. Its contribution to and potential as ice nucleating particle (INP) is still largely unknown though.  Here we will present offline samples of airborne aerosol taken in the Central Arctic during MOSAiC focussing on the transition period from winter to spring. The samples comprise of quasi-ciontinuous low-volume air filter samples taken in the British Antarctic Survey’s aerosol lab container on board of RV Polarstern, weekly snow samples from the ice floe, and filter samples taken by tethered balloon. These samples were analysed for their ice nucleating characteristics using a peltier cold stage and applying a machine learning algorithm to the images taken during the cooling process.Initial results confirm an increased presence of INP in both the airborne and snow samples at the turn from winter to spring. 
Evolution of Atmospheric Oxygen Escape on Earth since the Paleoproterozoic Era
Authors: Alonso Tagle Maria Luisa , Maggiolo Romain , Gunell Herbert , Cessateur Gael , De Keyser Johan , Dandouras Iannis , Vidotto Aline , Borlina Caue , Nicholson Claire , Lapenta Giovanni , Pierrard Viviane , Vandaele Ann Carine
Published: March 2024

Understanding atmospheric escape over geological timescales is essential to constrain the planet’s ability to retain its atmosphere and thus sustain life. Atmospheric particles are energized through solar radiation and plasma interactions between the solar wind, the Earth’s magnetosphere, and ionosphere, escaping through various mechanisms.For Earth, several missions provided measurements of the oxygen escape rate. However, measurements are for current solar and planetary conditions that strongly differ from the past conditions (e.g. stronger solar wind, higher solar EUV radiation). The main challenge to estimate the past escape rate is to extrapolate current measurements to the past solar system environment.Since the Great Oxidation Event, 2.45 Gyr. ago, there is a significant amount of oxygen in the atmosphere. The goal of this study is to assess the stability of oxygen on Earth concerning atmospheric escape. We developed a semi-empirical model, which considers seven different escape mechanisms to estimate the net oxygen escape on Earth since this event. We use models available in the literature to describe the past solar wind and solar radiation, the Earth’s magnetic moment history, and the Earth’s exosphere evolution due to the change of solar EUV radiation while considering a constant atmospheric composition. The escape rate is calculated for these previous conditions considering a physical scaling and/or empirical formulas per mechanism.We estimate that the total oxygen loss during the last 2.45 Gyr., reaches almost 30% of the current atmospheric oxygen content. Oxygen escape is dominated by polar processes, polar wind and cusp escape, contributing over 90% of the total loss. Polar wind is the leading erosion mechanism before ~1.5 Gyr. while escape from the polar cusp dominates at present.
Using ERA-5 reanalysis to characterize extreme rainfall in Italy
Authors: Chiaravalloti Francesco , Coscarelli Roberto , Caloiero Tommaso
Published: March 2024

Heavy precipitation events are likely to become more frequent in most parts of Europe; yet, records of hourly precipitation are often insufficient to study trends and changes in heavy rainfall. Atmospheric reanalyses are an important source of long-term meteorological data, often considered as a solution to overcome the unavailability of direct measurements. The reanalysis procedure makes use of a large amount of heterogeneous historical observations, both sensed and remotely measured (in situ, satellite, etc), assimilated within a dynamical model to reconstruct the state of the atmosphere, land surface and oceans in the past. Among the available reanalyses, the ERA5 dataset released by the ECMWF, can be considered one of the state-of-the-art products. Atmospheric and surface variables are provided hourly, from 1950 to almost real time, with a horizontal resolution of 31 km. The land model of the ERA5, driven by the downscaled meteorological forcing from the lowest ERA5 model level, and with an elevation correction for the thermodynamic near-surface state, is also used to derive the ERA5-land dataset, characterized by a higher spatial resolution (9 km) and finer precipitation distribution details.In this paper, data from the ERA5-land reanalysis dataset were used to characterize the 1-hour maximum yearly rainfall values in Italy. Specifically, 3215 grid series of 1-hour rainfall for the period 1950-2020 have been first extracted. Then, for each grid series the 71 1-hour maximum yearly rainfall values have been evaluated. Moreover, the time frame 1950-2020 has been divided into several intervals, and for each one, the frequency distribution of the months recording the annual maxima was calculated. Finally, a cluster analysis has been performed to evaluate the area with a similar monthly distribution of these values. Results showed that, considering the data over the whole of Italy, the monthly distribution of occurrences of annual maxima of 1-hr rainfall is characterized by a peak in September occurring in all the time windows considered. Furthermore, clustering cells with a similar distribution of annual hourly rainfall maxima, using k-means, allowed to identify three groups characterised by different months with the highest frequency of occurrence of the maximum.This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of 'Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.
A Matter of Scale: Thermodynamic and Large-Scale Constraints in Extreme Rainfall Under a Changing Climate
Authors: Andria Santa , Borga Marco , Marani Marco
Published: March 2024

Changes in the hydrological cycle and, in particular, in rainfall extreme events induced by global warming are expected to pose significantly increased hazards in the coming decades. However, changes in the probability of occurrence of intense precipitation remain poorly understood even in observations. Here we investigate the thermodynamic and large-scale constraints to the generation of extreme rainfall at both hourly and daily scales. To this aim, we address some of the ambiguities intrinsic to the traditional definition of the dependence of extreme rainfall on temperature as mediated by the Clausius-Clapeyron (CC) relation. For this purpose, we use a non-asymptotic extreme value distribution (Marani and Ignaccolo, 2015) as a basis for our analysis. In this framework, the distribution of extremes emerges from the distribution of the ordinary events, here allowed to vary under climate change. The distribution of annual maxima is expressed as a function of the probability distribution of all events (that may be inferred using most of the available data, rather than just on yearly maxima) and of the number of event occurrences per year. The rationale here is that a warming of the atmosphere will affect the distribution of all rainfall events, i.e. the shape of the ordinary event distribution, rather than just rainfall extremes as in traditional CC arguments. Based on this approach, we then analyze the relation between the parameters of the probability distribution of ordinary precipitation events and temperature at the daily and hourly scales, using observational data in Padova, Italy (where almost 300 years of observations are available) and multiple stations in the continental US.While local temperature is widely considered to be a major driver of change in rainfall regimes, changes in large-scale circulation are also expected to play a significant role in shaping future rainfall regimes. In order to represent the effects of large-scale circulation, and analyze changes that remain unexplained by local temperature, we compute here the Vertically Integrated Moisture Convergence, derived from the ECMWF Reanalysis v5 (ERA5) dataset.Our results indicate that hourly precipitation is mainly controlled by thermodynamics, with the scale parameter of the probability distribution of hourly precipitation intensity showing a CC dependence. Conversely, at the daily scale, we show that precipitation variability is not explained by temperature changes but is rather driven by other factors such as large-scale circulation. These results support the need for an integrated approach, which quantitatively accounts for both local thermodynamics and large-scale circulation to estimate future changes in daily precipitation extremes under a climate change.
The composition and sources of airborne bacteria and proteinaceous Ice Nucleating Particles in the High Arctic marine region during Spring
Authors: Schmidt Jennie Spicker , Glasius Marianne , Mavis Camille , Creamean Jessie , Freitas Gabriel , Zieger Paul , Finster Kai , Šantl-Temkiv Tina
Published: March 2024

The Arctic is a particularly vulnerable region on Earth, where climate change takes place at an intense pace. Clouds represent an essential element within the Arctic atmosphere and play a crucial role in the regional radiative balance. The physical properties of clouds are tightly interlinked with the presence of aerosols that can serve as cloud condensation nuclei (CCN) and as ice nucleating particles (INPs), which facilitate the formation of cloud droplets and ice crystals, respectively. Consequently, they affect cloud thickness, lifetime, and albedo.More studies propose that various biological aerosols e.g., aerosolized microbial cells, proteinaceous compounds and fragments actively contribute to cloud processes serving as INPs active at high subzero temperatures (>-15°C). However, our understanding of microorganisms responsible for producing compounds serving as INPs, their source environments, and their level of activity, remains highly uncertain.Given the profound impact of climate change in the Arctic region, understanding the role of biological INPs in the atmosphere becomes particularly critical during Arctic melt season. Here, we present an overview of bioaerosol observations and sources tracking from the recent Arctic expedition ”Atmospheric rivers and the onset of Arctic melt” (ARTofMELT 2023).Biological INPs are thought to originate from the ocean and meltwater sources during the Arctic Spring and Summer. To assess the potential contribution of these sources to INP active aerosols, aerosols were generated from bulk seawater and sea ice melt water with a temperature-controlled sea spray simulation chamber. The presence of microorganisms in the bulk water and aerosol was quantified using flow cytometry and qPCR while the composition of the microbial communities was determined by amplicon sequencing. Additionally, fluorescent bioaerosols generated by the chamber were  analyzed using a Multiparameter Bioaerosol Spectrometer (MBS). Simultaneously, ambient air samples were analyzed for the presence of microbial cells, bioaerosols, and the composition of the collected microbial community. The ice nucleating properties of water, sea ice melt, and aerosols from the chamber and ambient aerosol were also measured to determine their relevance for Arctic cloud formation.Preliminary results from the ambient measurements revealed low concentrations of airborne bacterial cells and highly active INPs. From the sea spray simulations, we found that ice melt, snow melt and seawater samples generated a high flux of bacterial cells which were accompanied by INPs active predominantly at low freezing temperatures (
Source regions of irregularities causing GNSS radio scintillation: An investigation using EISCAT
Authors: Madhanakumar Mahith , Spicher Andres , Vierinen Juha , Oksavik Kjellmar
Published: March 2024

Scintillation causing irregularities can occur in the E region or the F region ionosphere or even in both. This is due to the stochastic nature of the ionosphere that varies with the time of day, latitudes, seasons, solar and geomagnetic effects, etc. The relative importance of the different regions in hosting irregularities that can cause scintillation at GNSS frequencies in the dayside high latitude ionosphere is yet to be established. This study makes use of the 32-m European Incoherent SCATer (EISCAT) radar on Svalbard (ESR) to identify the ionospheric signatures during scintillation events. In particular, it takes advantage of the fast-scanning capability of ESR allowing it to image large areas of the ionosphere. This allows to capture ionospheric phenomena over a wide range of geographic latitudes in a short span of time. The scintillation measurements are obtained from the receiver installed at the nearby Kjell Henriksen Observatory (KHO) which can track signals from multiple GNSS constellations simultaneously. By combining the radar observations with scintillation measurements, the source regions responsible for GNSS scintillation at the dayside auroral/cusp regions are identified and characterized. The results are discussed in the context of nighttime statistics when patches and auroral dynamics are responsible for strong scintillation in GNSS signals. The results shown help better understand the impact of ionospheric irregularities on radio wave propagation in the high latitude ionosphere. Furthermore, the capability of extending the analysis to the upcoming EISCAT 3D using a simultaneous multi-beam multi-direction pattern is emphasized.
Structural Mode and Evolution of Multi-stage Normal Fault Development in Jieyang Depression, NE South China Sea
Authors: Wang Chao-Hsun , Wu Ping-Rong , Yang Kenn-Ming , Yang Chih-Cheng , Hsieh Bieng-Zih
Published: March 2024

Jieyang Depression is located between the Southern Depression of the Taixinan Basin and the Chaoshan Depression of the Pearl River Mouth Basin in northern South China Sea. A series of NE-SW striking half graben developed in this area from late Mesozoic to early Paleogene, and then another two stages of normal faulting happened during Neogene. The development of these stages of normal faults are separated by the breakup unconformity and a post-rift truncation. The main purposes of this study are to investigate the evolutionary sequences of the multi-stages of normal faults and the truncations during the multi-stages of extension, the spatial distribution of each phase of normal faults, and how the younger normal faults were affected by the pre-existing ones.The normal faults in the Jieyang Depression can be divided into three types. Type 1 faults are related to the Paleogene half graben formation and only cut through the pre-rift and syn-rift strata. Type 2 normal faults only developed and cut through the post-rift strata. Type 3 normal faults cut through the syn-rift and post-rift strata. In this study, we further divide the Type 2 normal faults into two different kinds. Type 2-1 faults developed above the Paleogene half graben and cut off by the post-rift truncation. Type 2-2 faults developed after the truncation. Type 3 normal faults can be divided into two different kinds as well. Type 3-1 faults developed in the syn-rift stage and yet were reactivated and linked with the faults developing in the later extension. Type 3-2 faults are the Type 2 faults that developed continuously cutting downward into the syn-rift strata.In terms of spatial distribution, Type 1 normal faults strike NE-SW, forming the half grabens. Most of the Type 2 normal faults are located far away from continental shelf. Type 3 normal faults mostly distribute on the northeast and northwest sides of the study area, close to the continental shelf, and most of them are cut by the post-rift truncation.As a result, from the late Mesozoic to the early Paleogene, the northern slope of the South China Sea experienced a NW-SE extension. At the end of the Paleogene, the extension ceased, forming the breakup unconformity. In the Neogene, the Jieyang Depression experienced second extension. In this time the extension orientation was NNW-SSE, developing Type 2-1 and Type 3 faults before the post-rift truncation. The subsequent truncation cuts Type 2-1 faults and the upper part of the Type 3 faults. After accumulating new strata above truncation, the third stage of extension happened and Type 2-2 faults developed after the post-rift truncation.Key words: South China Sea, Jieyang Depression, normal fault, multi-stage extension, truncation